diff --git a/Makefile b/Makefile
index 63422235..c165094e 100644
--- a/Makefile
+++ b/Makefile
@@ -3,10 +3,10 @@
 docs:
 	(cd docs && make html)
 
-lint:
+type_check:
 	mypy lcs
 
-test: lint
+test: type_check
 	py.test -n 4 --pep8 -m pep8
 	py.test -n 4 --cov=lcs tests/
 
diff --git a/docs/source/notebooks/checkerboard_nd3.pdf b/docs/source/notebooks/checkerboard_nd3.pdf
new file mode 100644
index 00000000..bb86f0d8
Binary files /dev/null and b/docs/source/notebooks/checkerboard_nd3.pdf differ
diff --git a/docs/source/notebooks/foo.pdf b/docs/source/notebooks/foo.pdf
new file mode 100644
index 00000000..ef402966
Binary files /dev/null and b/docs/source/notebooks/foo.pdf differ
diff --git a/docs/source/notebooks/rACS in Real-Multiplexer.ipynb b/docs/source/notebooks/rACS in Real-Multiplexer.ipynb
index 489b8f1f..44da52c1 100644
--- a/docs/source/notebooks/rACS in Real-Multiplexer.ipynb	
+++ b/docs/source/notebooks/rACS in Real-Multiplexer.ipynb	
@@ -17,6 +17,8 @@
     "import logging\n",
     "logging.basicConfig(level=logging.INFO)\n",
     "\n",
+    "from itertools import groupby\n",
+    "\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
@@ -31,7 +33,6 @@
     "%autoreload 2\n",
     "\n",
     "# Load PyALCS module\n",
-    "from lcs.representations.RealValueEncoder import RealValueEncoder\n",
     "from lcs.agents.racs import RACS, Configuration\n",
     "\n",
     "# Load OpenAI environments\n",
@@ -74,7 +75,7 @@
     {
      "data": {
       "text/plain": [
-       "[0.3725874283604559, 0.9052590538764941, 0.15552403628446665, 0]"
+       "[0.6787622956334223, 0.9544450977481421, 0.2340574547788823, 0.0]"
       ]
      },
      "execution_count": 3,
@@ -102,7 +103,7 @@
     {
      "data": {
       "text/plain": [
-       "[0, 1, 0, 0]"
+       "[1, 1, 0, 0]"
       ]
      },
      "execution_count": 4,
@@ -172,212 +173,6 @@
     "print(f\"Lower bounds: {rmpx.observation_space.high}\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## rACS\n",
-    "\n",
-    "- write abount _encoders_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>1-bit</th>\n",
-       "      <th>2-bit</th>\n",
-       "      <th>3-bit</th>\n",
-       "      <th>4-bit</th>\n",
-       "      <th>5-bit</th>\n",
-       "      <th>6-bit</th>\n",
-       "      <th>7-bit</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Perception</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0.0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.1</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.2</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "      <td>12</td>\n",
-       "      <td>25</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.3</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "      <td>9</td>\n",
-       "      <td>19</td>\n",
-       "      <td>38</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.4</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "      <td>12</td>\n",
-       "      <td>25</td>\n",
-       "      <td>51</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.5</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "      <td>8</td>\n",
-       "      <td>16</td>\n",
-       "      <td>32</td>\n",
-       "      <td>64</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.6</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "      <td>9</td>\n",
-       "      <td>19</td>\n",
-       "      <td>38</td>\n",
-       "      <td>76</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.7</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>11</td>\n",
-       "      <td>22</td>\n",
-       "      <td>44</td>\n",
-       "      <td>89</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.8</th>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "      <td>12</td>\n",
-       "      <td>25</td>\n",
-       "      <td>51</td>\n",
-       "      <td>102</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.9</th>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>7</td>\n",
-       "      <td>14</td>\n",
-       "      <td>28</td>\n",
-       "      <td>57</td>\n",
-       "      <td>115</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>7</td>\n",
-       "      <td>15</td>\n",
-       "      <td>31</td>\n",
-       "      <td>63</td>\n",
-       "      <td>127</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            1-bit  2-bit  3-bit  4-bit  5-bit  6-bit  7-bit\n",
-       "Perception                                                 \n",
-       "0.0             0      0      0      0      0      0      0\n",
-       "0.1             0      0      0      1      3      6     12\n",
-       "0.2             0      0      1      3      6     12     25\n",
-       "0.3             0      1      2      4      9     19     38\n",
-       "0.4             0      1      3      6     12     25     51\n",
-       "0.5             1      2      4      8     16     32     64\n",
-       "0.6             1      2      4      9     19     38     76\n",
-       "0.7             1      2      5     11     22     44     89\n",
-       "0.8             1      3      6     12     25     51    102\n",
-       "0.9             1      3      7     14     28     57    115\n",
-       "1.0             1      3      7     15     31     63    127"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def encode(bits, val):\n",
-    "    return int(RealValueEncoder(bits).encode(val))\n",
-    "\n",
-    "\n",
-    "r = np.arange(0, 1.1, .1)\n",
-    "\n",
-    "df = pd.DataFrame(r, columns=['Perception'])\n",
-    "\n",
-    "for bit in [1, 2, 3, 4, 5, 6, 7]:\n",
-    "    df[f'{bit}-bit'] = df.apply(lambda row: encode(bit, row['Perception']), axis=1)\n",
-    "\n",
-    "df.set_index('Perception', inplace=True)\n",
-    "df"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -388,7 +183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -410,25 +205,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def perform_experiment(env, encoder_bits, trials):\n",
+    "def perform_experiment(env, trials):\n",
     "    # create configuration\n",
     "    cfg = Configuration(env.observation_space.shape[0], env.action_space.n,\n",
-    "                    encoder=RealValueEncoder(encoder_bits),\n",
     "                    metrics_trial_frequency=5,\n",
     "                    user_metrics_collector_fcn=rmpx_metrics,\n",
     "                    epsilon=1.0,  # no biased exploration\n",
     "                    do_ga=True,\n",
     "                    theta_r=0.9,  # reliablity threshold\n",
-    "                    theta_i=0.2,  # inadequacy threshold\n",
-    "                    theta_ga=100,\n",
-    "                    cover_noise=0,\n",
-    "                    mutation_noise=0.25,\n",
-    "                    chi=1.0,  # cross-over probability\n",
-    "                    mu=0.1)  # mutation probability\n",
+    "                    theta_i=0.3,  # inadequacy threshold\n",
+    "                    theta_ga=25,\n",
+    "                    cover_noise=0.1,\n",
+    "                    mutation_noise=0.5,\n",
+    "                    chi=0.5,  # cross-over probability\n",
+    "                    mu=1)  # mutation probability\n",
     "    \n",
     "    # create agent\n",
     "    agent = RACS(cfg)\n",
@@ -448,7 +242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -464,11 +258,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def plot_results(metrics, env, encoder_bits):\n",
+    "def plot_results(metrics, env):\n",
     "    # parse metrics into data frame\n",
     "    df = parse_metrics(metrics)\n",
     "    \n",
@@ -477,7 +271,7 @@
     "    exploit_df = df[df['mode'] == 'exploit']\n",
     "    \n",
     "    f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n",
-    "    f.suptitle(f\"{env.env.spec.id}, ubr encoder bits: {encoder_bits}\", fontsize=14)\n",
+    "    f.suptitle(f\"{env.env.spec.id}\", fontsize=14)\n",
     "    \n",
     "    # plot 1 - average reward\n",
     "    explore_df['reward'].rolling(window=50).mean().plot(label='explore', ax=ax1)\n",
@@ -503,7 +297,7 @@
     "    ax2.set_ylabel('# Classifiers')\n",
     "    ax2.legend()\n",
     "    \n",
-    "    f.savefig(\"foo.pdf\", bbox_inches='tight')"
+    "    #f.savefig(\"foo.pdf\", bbox_inches='tight')"
    ]
   },
   {
@@ -515,13 +309,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def evaluate(rmpx, encoder_bits, trials=10_000):\n",
-    "    population, metrics = perform_experiment(rmpx, encoder_bits=encoder_bits, trials=trials)\n",
-    "    plot_results(metrics, rmpx, encoder_bits)\n",
+    "def evaluate(rmpx, trials=10_000):\n",
+    "    population, metrics = perform_experiment(rmpx, trials=trials)\n",
+    "    plot_results(metrics, rmpx)\n",
     "    \n",
     "    # sort classifiers in population according to action\n",
     "    population = sorted(population, key=lambda cl: cl.action)\n",
@@ -529,6 +323,18 @@
     "    return population, metrics"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def print_k_best(pop, k=10):\n",
+    "    pop = sorted(pop, key=lambda cl: -cl.fitness)\n",
+    "    for cl in pop[:k]:\n",
+    "        print(cl)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -556,6043 +362,39 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 1, 'reward': 0, 'population': 1, 'numerosity': 1, 'reliable': 0}\n",
-      "INFO:lcs.agents.Agent:{'trial': 5000, 'steps_in_trial': 1, 'reward': 1000, 'population': 13, 'numerosity': 154, 'reliable': 12}\n"
+      "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 1, 'reward': 1000, 'population': 1, 'numerosity': 1, 'reliable': 0}\n",
+      "INFO:lcs.agents.Agent:{'trial': 5000, 'steps_in_trial': 1, 'reward': 1000, 'population': 1017, 'numerosity': 1046, 'reliable': 0}\n",
+      "INFO:lcs.agents.Agent:{'trial': 10000, 'steps_in_trial': 1, 'reward': 0, 'population': 2020, 'numerosity': 2076, 'reliable': 0}\n",
+      "INFO:lcs.agents.Agent:{'trial': 15000, 'steps_in_trial': 1, 'reward': 0, 'population': 2997, 'numerosity': 3074, 'reliable': 0}\n",
+      "INFO:lcs.agents.Agent:{'trial': 20000, 'steps_in_trial': 1, 'reward': 1000, 'population': 4048, 'numerosity': 4157, 'reliable': 0}\n",
+      "INFO:lcs.agents.Agent:{'trial': 25000, 'steps_in_trial': 1, 'reward': 0, 'population': 5016, 'numerosity': 5153, 'reliable': 0}\n",
+      "INFO:lcs.agents.Agent:{'trial': 30000, 'steps_in_trial': 1, 'reward': 0, 'population': 6009, 'numerosity': 6175, 'reliable': 0}\n",
+      "INFO:lcs.agents.Agent:{'trial': 35000, 'steps_in_trial': 1, 'reward': 1000, 'population': 6973, 'numerosity': 7161, 'reliable': 0}\n",
+      "INFO:lcs.agents.Agent:{'trial': 40000, 'steps_in_trial': 1, 'reward': 1000, 'population': 7990, 'numerosity': 8207, 'reliable': 0}\n",
+      "INFO:lcs.agents.Agent:{'trial': 45000, 'steps_in_trial': 1, 'reward': 0, 'population': 9034, 'numerosity': 9272, 'reliable': 0}\n",
+      "INFO:lcs.agents.Agent:{'trial': 50000, 'steps_in_trial': 1, 'reward': 1000, 'population': 10063, 'numerosity': 10329, 'reliable': 0}\n",
+      "INFO:lcs.agents.Agent:{'trial': 55000, 'steps_in_trial': 1, 'reward': 0, 'population': 11101, 'numerosity': 11393, 'reliable': 0}\n",
+      "INFO:lcs.agents.Agent:{'trial': 60000, 'steps_in_trial': 1, 'reward': 0, 'population': 12143, 'numerosity': 12471, 'reliable': 0}\n",
+      "INFO:lcs.agents.Agent:{'trial': 65000, 'steps_in_trial': 1, 'reward': 1000, 'population': 13123, 'numerosity': 13476, 'reliable': 0}\n",
+      "INFO:lcs.agents.Agent:{'trial': 70000, 'steps_in_trial': 1, 'reward': 0, 'population': 14145, 'numerosity': 14515, 'reliable': 0}\n",
+      "INFO:lcs.agents.Agent:{'trial': 75000, 'steps_in_trial': 1, 'reward': 0, 'population': 15177, 'numerosity': 15577, 'reliable': 0}\n",
+      "INFO:lcs.agents.Agent:{'trial': 80000, 'steps_in_trial': 1, 'reward': 0, 'population': 16203, 'numerosity': 16640, 'reliable': 0}\n",
+      "INFO:lcs.agents.Agent:{'trial': 85000, 'steps_in_trial': 1, 'reward': 1000, 'population': 17231, 'numerosity': 17697, 'reliable': 0}\n",
+      "INFO:lcs.agents.Agent:{'trial': 90000, 'steps_in_trial': 1, 'reward': 0, 'population': 18228, 'numerosity': 18716, 'reliable': 0}\n",
+      "INFO:lcs.agents.Agent:{'trial': 95000, 'steps_in_trial': 1, 'reward': 1000, 'population': 19213, 'numerosity': 19722, 'reliable': 0}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 8.09 s, sys: 97.3 ms, total: 8.18 s\n",
-      "Wall time: 7.94 s\n"
+      "CPU times: user 1h 35min 1s, sys: 6.74 s, total: 1h 35min 8s\n",
+      "Wall time: 1h 35min 17s\n"
      ]
     },
     {
      "data": {
-      "text/plain": [
-       "([O.|.O|OO|OO\t0\tOO|OO|OO|OO x 20 fitness: 0.00,\n",
-       "  O.|O.|.O|O.\t0\tOO|OO|OO|.O x 20 fitness: 500.00,\n",
-       "  OO|O.|O.|O.\t0\tOO|OO|OO|.O x 4 fitness: 625.00,\n",
-       "  .O|OO|.O|OO\t0\tOO|OO|OO|OO x 20 fitness: 0.00,\n",
-       "  .O|OO|O.|O.\t0\tOO|OO|OO|.O x 17 fitness: 625.00,\n",
-       "  O.|O.|OO|OO\t1\tOO|OO|OO|OO x 4 fitness: 0.00,\n",
-       "  OO|O.|O.|OO\t1\tOO|OO|OO|OO x 15 fitness: 0.00,\n",
-       "  O.|.O|OO|O.\t1\tOO|OO|OO|.O x 20 fitness: 625.00,\n",
-       "  .O|OO|O.|OO\t1\tOO|OO|OO|OO x 7 fitness: 0.00,\n",
-       "  .O|OO|.O|O.\t1\tOO|OO|OO|.O x 18 fitness: 625.00,\n",
-       "  OO|O.|OO|OO\t1\tOO|OO|OO|OO x 1 fitness: 116.34,\n",
-       "  OO|O.|OO|O.\t1\tOO|OO|OO|.O x 1 fitness: 84.32],\n",
-       " [{'trial': 0,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 1,\n",
-       "   'numerosity': 1,\n",
-       "   'reliable': 0},\n",
-       "  {'trial': 5,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 2,\n",
-       "   'numerosity': 2,\n",
-       "   'reliable': 0},\n",
-       "  {'trial': 10,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 3,\n",
-       "   'numerosity': 3,\n",
-       "   'reliable': 0},\n",
-       "  {'trial': 15,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 4,\n",
-       "   'numerosity': 4,\n",
-       "   'reliable': 0},\n",
-       "  {'trial': 20,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 5,\n",
-       "   'numerosity': 5,\n",
-       "   'reliable': 0},\n",
-       "  {'trial': 25,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 6,\n",
-       "   'numerosity': 6,\n",
-       "   'reliable': 0},\n",
-       "  {'trial': 30,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 10,\n",
-       "   'numerosity': 10,\n",
-       "   'reliable': 0},\n",
-       "  {'trial': 35,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 11,\n",
-       "   'numerosity': 11,\n",
-       "   'reliable': 0},\n",
-       "  {'trial': 40,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 12,\n",
-       "   'numerosity': 12,\n",
-       "   'reliable': 0},\n",
-       "  {'trial': 45,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 13,\n",
-       "   'reliable': 0},\n",
-       "  {'trial': 50,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 18,\n",
-       "   'numerosity': 18,\n",
-       "   'reliable': 0},\n",
-       "  {'trial': 55,\n",
-       "   'steps_in_trial': 1,\n",
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-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 147,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4815,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 147,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4820,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 147,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4825,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 148,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4830,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 148,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4835,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 148,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4840,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 148,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4845,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 148,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4850,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 148,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4855,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 149,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4860,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 149,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4865,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 149,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4870,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 149,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4875,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 149,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4880,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 150,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4885,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 150,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4890,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 150,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4895,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 150,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4900,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 150,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4905,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 150,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4910,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 150,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4915,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 151,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4920,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 151,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4925,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 151,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4930,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 151,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4935,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 151,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4940,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 151,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4945,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 152,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4950,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 153,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4955,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 153,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4960,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 154,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4965,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 154,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4970,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 154,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4975,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 154,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4980,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 154,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4985,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 154,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4990,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 154,\n",
-       "   'reliable': 12},\n",
-       "  {'trial': 4995,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 154,\n",
-       "   'reliable': 12},\n",
-       "  ...])"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 2 Axes>"
       ]
@@ -6603,47 +405,34 @@
    ],
    "source": [
     "%%time\n",
-    "evaluate(rmpx3, encoder_bits=1, trials=10_000)"
+    "\n",
+    "pop, metrics = evaluate(rmpx3, trials=100_000)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "scrolled": true
-   },
+   "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 1, 'reward': 1000, 'population': 1, 'numerosity': 1, 'reliable': 0}\n",
-      "INFO:lcs.agents.Agent:{'trial': 5000, 'steps_in_trial': 1, 'reward': 0, 'population': 92, 'numerosity': 574, 'reliable': 47}\n",
-      "INFO:lcs.agents.Agent:{'trial': 10000, 'steps_in_trial': 1, 'reward': 0, 'population': 51, 'numerosity': 538, 'reliable': 50}\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 33.2 s, sys: 117 ms, total: 33.4 s\n",
-      "Wall time: 33.2 s\n"
+      "[0.00;1.00][0.00;1.00][0.04;0.04][0.00;1.00]\t1\t[0.00;1.00][0.00;1.00][0.00;1.00][0.00;1.00] x 1 fitness: 351.62\n",
+      "[0.83;0.83][0.00;1.00][0.00;1.00][0.00;1.00]\t1\t[0.00;1.00][0.00;1.00][0.00;1.00][0.00;1.00] x 1 fitness: 338.39\n",
+      "[0.00;1.00][0.00;1.00][0.67;0.67][0.00;1.00]\t0\t[0.00;1.00][0.00;1.00][0.00;1.00][0.00;1.00] x 1 fitness: 335.96\n",
+      "[0.08;0.08][0.00;1.00][0.00;1.00][0.00;1.00]\t0\t[0.00;1.00][0.00;1.00][0.00;1.00][0.00;1.00] x 1 fitness: 332.73\n",
+      "[0.07;0.07][0.00;1.00][0.00;1.00][0.00;1.00]\t0\t[0.00;1.00][0.00;1.00][0.00;1.00][0.00;1.00] x 1 fitness: 332.32\n",
+      "[0.00;1.00][0.00;1.00][0.56;0.56][0.00;1.00]\t0\t[0.00;1.00][0.00;1.00][0.00;1.00][0.00;1.00] x 1 fitness: 328.77\n",
+      "[0.21;0.21][0.00;1.00][0.00;1.00][0.00;1.00]\t1\t[0.00;1.00][0.00;1.00][0.00;1.00][0.00;1.00] x 1 fitness: 328.50\n",
+      "[0.00;1.00][0.00;1.00][0.78;0.78][0.00;1.00]\t0\t[0.00;1.00][0.00;1.00][0.00;1.00][0.00;1.00] x 1 fitness: 328.40\n",
+      "[0.00;1.00][0.00;1.00][0.34;0.34][0.00;1.00]\t1\t[0.00;1.00][0.00;1.00][0.00;1.00][0.00;1.00] x 1 fitness: 328.34\n",
+      "[0.00;1.00][0.00;1.00][0.75;0.75][0.00;1.00]\t0\t[0.00;1.00][0.00;1.00][0.00;1.00][0.00;1.00] x 1 fitness: 328.19\n"
      ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "%%time\n",
-    "pop3, m3 = evaluate(rmpx3, encoder_bits=2, trials=15_000)"
+    "print_k_best(pop)"
    ]
   },
   {
@@ -6655,21259 +444,62 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "Action: 0, classifiers: 30\n",
-      "OOOO|OO..|OO..|O...\t0\tOOOO|OOOO|OOOO|...O x 6 fitness: 551.37\n",
-      "OOOO|OO..|O...|O...\t0\tOOOO|OOOO|OOOO|...O x 2 fitness: 500.00\n",
-      "O...|OO..|OOOO|O...\t0\tOOOO|OOOO|OOOO|...O x 16 fitness: 499.73\n",
-      "...O|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|...O x 15 fitness: 437.50\n",
-      "OOOO|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|...O x 1 fitness: 437.50\n",
-      "...O|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|...O x 18 fitness: 437.50\n",
-      "..O.|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|...O x 13 fitness: 437.50\n",
-      "..O.|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|...O x 15 fitness: 410.79\n",
-      ".O..|.O..|.OOO|O...\t0\tOOOO|OOOO|OOOO|...O x 16 fitness: 370.75\n",
-      ".O..|OO..|...O|O...\t0\tOOOO|OOOO|OOOO|...O x 4 fitness: 312.26\n",
-      "OO..|O...|...O|O...\t0\tOOOO|OOOO|OOOO|...O x 1 fitness: 311.86\n",
-      "O...|.O..|...O|O...\t0\tOOOO|OOOO|OOOO|...O x 1 fitness: 248.98\n",
-      "...O|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|...O x 1 fitness: 248.69\n",
-      "..O.|...O|.O..|O...\t0\tOOOO|OOOO|OOOO|...O x 1 fitness: 248.16\n",
-      "..O.|...O|O...|O...\t0\tOOOO|OOOO|OOOO|...O x 4 fitness: 248.02\n",
-      ".O..|.O..|..O.|O...\t0\tOOOO|OOOO|OOOO|...O x 3 fitness: 247.73\n",
-      ".O..|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|...O x 2 fitness: 247.58\n",
-      "...O|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|...O x 1 fitness: 247.31\n",
-      ".O..|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|...O x 19 fitness: 247.05\n",
-      "...O|...O|O...|O...\t0\tOOOO|OOOO|OOOO|...O x 1 fitness: 246.83\n",
-      "O...|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|...O x 1 fitness: 246.70\n",
-      "..O.|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|...O x 5 fitness: 245.53\n",
-      "O...|.O..|..O.|O...\t0\tOOOO|OOOO|OOOO|...O x 1 fitness: 245.30\n",
-      "..O.|OOOO|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO x 20 fitness: 0.00\n",
-      "...O|OOOO|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO x 20 fitness: 0.00\n",
-      "..O.|OOOO|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO x 20 fitness: 0.00\n",
-      "O...|..O.|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO x 20 fitness: 0.00\n",
-      ".O..|..O.|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO x 20 fitness: 0.00\n",
-      "...O|OOOO|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO x 20 fitness: 0.00\n",
-      "OO..|...O|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO x 20 fitness: 0.00\n",
-      "\n",
-      "Action: 1, classifiers: 16\n",
-      "OO..|...O|OOOO|O...\t1\tOOOO|OOOO|OOOO|...O x 20 fitness: 500.00\n",
-      "..OO|OOOO|...O|O...\t1\tOOOO|OOOO|OOOO|...O x 20 fitness: 500.00\n",
-      "O...|..O.|OOOO|O...\t1\tOOOO|OOOO|OOOO|...O x 20 fitness: 437.50\n",
-      ".O..|..O.|OOOO|O...\t1\tOOOO|OOOO|OOOO|...O x 20 fitness: 437.50\n",
-      "...O|OOOO|..O.|O...\t1\tOOOO|OOOO|OOOO|...O x 19 fitness: 437.50\n",
-      "..O.|OOOO|..O.|O...\t1\tOOOO|OOOO|OOOO|...O x 18 fitness: 437.50\n",
-      ".O..|O...|..O.|OOOO\t1\tOOOO|OOOO|OOOO|OOOO x 2 fitness: 9.71\n",
-      ".O..|O...|...O|OOOO\t1\tOOOO|OOOO|OOOO|OOOO x 4 fitness: 8.28\n",
-      "O...|O...|OOOO|OOOO\t1\tOOOO|OOOO|OOOO|OOOO x 4 fitness: 0.00\n",
-      "O...|.O..|OOOO|OOOO\t1\tOOOO|OOOO|OOOO|OOOO x 20 fitness: 0.00\n",
-      ".O..|.O..|OOOO|OOOO\t1\tOOOO|OOOO|OOOO|OOOO x 20 fitness: 0.00\n",
-      "..O.|OOOO|.O..|OOOO\t1\tOOOO|OOOO|OOOO|OOOO x 20 fitness: 0.00\n",
-      ".O..|O...|OOOO|OOOO\t1\tOOOO|OOOO|OOOO|OOOO x 14 fitness: 0.00\n",
-      "...O|OOOO|.O..|OOOO\t1\tOOOO|OOOO|OOOO|OOOO x 20 fitness: 0.00\n",
-      "..OO|.OOO|O...|OOOO\t1\tOOOO|OOOO|OOOO|OOOO x 20 fitness: 0.00\n",
-      "OOOO|O...|O...|OOOO\t1\tOOOO|OOOO|OOOO|OOOO x 9 fitness: 0.00\n"
+      "46\n"
      ]
     }
    ],
    "source": [
-    "from itertools import groupby\n",
+    "from lcs.representations import Interval\n",
     "\n",
-    "rel = [cl for cl in pop3 if cl.is_reliable()]\n",
+    "generic_ones = []\n",
     "\n",
-    "for k, g in groupby(rel, key=lambda cl: cl.action):\n",
-    "    niche = list(g)\n",
-    "    print(f'\\nAction: {k}, classifiers: {len(niche)}')\n",
-    "    for cl in sorted(niche, key=lambda cl: -cl.fitness):\n",
-    "        print(f'{cl}')"
+    "for p in pop:\n",
+    "    for c in p.condition:\n",
+    "        if c.p != c.q and c != Interval(0., 1.):\n",
+    "            generic_ones.append(p)\n",
+    "            break\n",
+    "\n",
+    "print(len(generic_ones))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 1, 'reward': 1000, 'population': 1, 'numerosity': 1, 'reliable': 0}\n",
-      "INFO:lcs.agents.Agent:{'trial': 5000, 'steps_in_trial': 1, 'reward': 0, 'population': 1568, 'numerosity': 2901, 'reliable': 32}\n",
-      "INFO:lcs.agents.Agent:{'trial': 10000, 'steps_in_trial': 1, 'reward': 1000, 'population': 1509, 'numerosity': 3589, 'reliable': 103}\n",
-      "INFO:lcs.agents.Agent:{'trial': 15000, 'steps_in_trial': 1, 'reward': 0, 'population': 1466, 'numerosity': 4259, 'reliable': 113}\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 8min 55s, sys: 4.84 s, total: 9min\n",
-      "Wall time: 9min 40s\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "([.......O|.O......|....O...|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 2 fitness: 40.08,\n",
-       "  ....O...|...O....|.......O|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 55.76,\n",
-       "  ......O.|OOOOOOOO|OOOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 169.86,\n",
-       "  ...O....|..O.....|....O...|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 67.48,\n",
-       "  ...O....|..O.....|OOOOOOOO|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 286.27,\n",
-       "  O.......|...O....|......O.|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 80.08,\n",
-       "  O.......|...O....|OOOOOOOO|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 235.19,\n",
-       "  OOOOOOOO|...O....|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 266.06,\n",
-       "  ......O.|OOOOOOOO|.......O|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 15 fitness: 5.81,\n",
-       "  ..O.....|...O....|OOOOOOOO|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 293.39,\n",
-       "  ......O.|OOOOOOOO|......O.|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 20 fitness: 7.05,\n",
-       "  .....O..|OOOOOOOO|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 287.86,\n",
-       "  ..O.....|..O.....|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 74.37,\n",
-       "  ..O.....|..O.....|OOOOOOOO|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 312.97,\n",
-       "  OOOOOOOO|..O.....|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 360.47,\n",
-       "  ......O.|.O......|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 3 fitness: 83.21,\n",
-       "  ......O.|OOOOOOOO|.O......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 4 fitness: 199.81,\n",
-       "  ...O....|.O......|....O...|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 95.74,\n",
-       "  ...O....|.O......|OOOOOOOO|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 3 fitness: 285.38,\n",
-       "  ...O....|.O......|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 69.08,\n",
-       "  ...O....|...O....|..O.....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 87.91,\n",
-       "  ...O....|...O....|OOOOOOOO|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 277.92,\n",
-       "  OOOOOOOO|O.......|...O....|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|....OOO. x 1 fitness: 269.59,\n",
-       "  ...O....|.O......|......O.|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 101.21,\n",
-       "  ...O....|OOOOOOOO|......O.|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 83.76,\n",
-       "  .O......|OOOOOOOO|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 126.50,\n",
-       "  ..O.....|O.......|.....O..|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 83.21,\n",
-       "  ....O...|OOOOOOOO|......O.|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 18 fitness: 5.91,\n",
-       "  OOOOOOOO|.O......|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 283.98,\n",
-       "  .....O..|..O.....|....O...|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 2 fitness: 78.95,\n",
-       "  ......O.|..O.....|....O...|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 87.32,\n",
-       "  ......O.|OOOOOOOO|....O...|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 13 fitness: 7.36,\n",
-       "  O.......|..O.....|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 5 fitness: 95.84,\n",
-       "  O.......|..O.....|OOOOOOOO|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 5 fitness: 293.03,\n",
-       "  ......O.|.O......|....O...|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 87.95,\n",
-       "  O.......|......O.|...O....|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 2 fitness: 63.28,\n",
-       "  O.......|......O.|OOOOOOOO|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 16 fitness: 5.35,\n",
-       "  .O......|......O.|..O.....|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 87.00,\n",
-       "  .O......|......O.|OOOOOOOO|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 16 fitness: 5.07,\n",
-       "  ..O.....|......O.|......O.|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 80.05,\n",
-       "  ..O.....|......O.|OOOOOOOO|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 17 fitness: 5.06,\n",
-       "  ...O....|....O...|OOOOOOOO|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 20 fitness: 5.34,\n",
-       "  OOOOOOOO|.......O|....O...|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 2 fitness: 3.82,\n",
-       "  .O......|...O....|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 79.33,\n",
-       "  ......O.|OOOOOOOO|.....O..|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 20 fitness: 7.11,\n",
-       "  ......O.|.O......|.....O..|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 2 fitness: 64.46,\n",
-       "  O.......|..O.....|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 76.33,\n",
-       "  OOOOOOOO|..O.....|.O......|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 286.00,\n",
-       "  .....O..|......O.|..O.....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 68.17,\n",
-       "  ......O.|O.......|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 77.24,\n",
-       "  O.......|...O....|.....O..|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 57.39,\n",
-       "  O.......|OOOOOOOO|.OOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 120.67,\n",
-       "  ......O.|......O.|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 69.46,\n",
-       "  ......O.|......O.|OOOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 66.08,\n",
-       "  .......O|OOOOOOOO|.OOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 76.40,\n",
-       "  ....O...|O.......|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 82.17,\n",
-       "  ....O...|OOOOOOOO|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 3 fitness: 289.50,\n",
-       "  ....O...|OOOOOOOO|.OOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 68.27,\n",
-       "  .O......|.......O|OOOOOOOO|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 17 fitness: 3.10,\n",
-       "  OOOOOOOO|......O.|....O...|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 5 fitness: 4.40,\n",
-       "  ....O...|....O...|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 57.66,\n",
-       "  ....O...|....O...|.OOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 57.75,\n",
-       "  ..O.....|...O....|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 78.43,\n",
-       "  OOOOOOOO|...O....|.OOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 186.83,\n",
-       "  ...O....|..O.....|..O.....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 71.20,\n",
-       "  ...O....|OOOOOOOO|.OOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 96.28,\n",
-       "  ...O....|O.......|.....O..|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 78.62,\n",
-       "  ...O....|OOOOOOOO|.....O..|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 104.39,\n",
-       "  ....O...|OOOOOOOO|.....O..|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 20 fitness: 7.74,\n",
-       "  ....O...|OOOOOOOO|.......O|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 20 fitness: 3.47,\n",
-       "  O.......|O.......|OOOOOOOO|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|....OOO. x 6 fitness: 330.84,\n",
-       "  ...O....|..O.....|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 90.43,\n",
-       "  OOOOOOOO|..O.....|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 249.94,\n",
-       "  .......O|OOOOOOOO|......O.|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 19 fitness: 6.93,\n",
-       "  ....O...|OOOOOOOO|....O...|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 17 fitness: 2.19,\n",
-       "  .......O|..O.....|.OOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 94.34,\n",
-       "  .O......|O.......|OOOOOOOO|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|....OOO. x 10 fitness: 353.22,\n",
-       "  .O......|.O......|OOOOOOOO|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 5 fitness: 309.55,\n",
-       "  ....O...|OOOOOOOO|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 298.23,\n",
-       "  ....O...|...O....|.OOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 102.78,\n",
-       "  ......O.|....O...|O.......|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 4 fitness: 75.02,\n",
-       "  O.......|..O.....|.OOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 114.25,\n",
-       "  .......O|......O.|.OOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 75.16,\n",
-       "  ......O.|OOOO....|.OOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 99.84,\n",
-       "  .....O..|OOOO....|.OOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 118.61,\n",
-       "  .......O|O.......|....O...|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 94.22,\n",
-       "  ..O.....|....O...|OOOOOOOO|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 20 fitness: 1.96,\n",
-       "  ..O.....|...O....|......O.|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 87.75,\n",
-       "  ..O.....|OOOO....|.OOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 14 fitness: 358.36,\n",
-       "  O.......|O.......|.OOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 147.65,\n",
-       "  O.......|OO......|OOOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 314.41,\n",
-       "  O.......|OOOO....|.OOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 8 fitness: 298.12,\n",
-       "  .....O..|.....O..|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 42.03,\n",
-       "  .....O..|OOOOOOOO|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 208.47,\n",
-       "  .....O..|...OOOOO|.O......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 200.93,\n",
-       "  ..O.....|..O.....|.....O..|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 80.53,\n",
-       "  ...O....|.O......|.OOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 132.96,\n",
-       "  ...O....|OO......|OOOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 302.33,\n",
-       "  ...O....|OOOO....|.OOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 14 fitness: 340.55,\n",
-       "  ...O....|.....O..|OOOOOOOO|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 16 fitness: 5.24,\n",
-       "  .......O|......O.|O.......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 4 fitness: 94.72,\n",
-       "  .O......|.O......|......O.|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 3 fitness: 66.28,\n",
-       "  .O......|OOOO....|.OOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 13 fitness: 339.48,\n",
-       "  OOOOOOOO|...O....|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 282.91,\n",
-       "  OOOOOOOO|.O......|..O.....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 324.33,\n",
-       "  OOOOOOOO|OO......|.O......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 5 fitness: 291.50,\n",
-       "  OOOOOOOO|.O......|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 251.00,\n",
-       "  ....O...|OOOOOOOO|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 7 fitness: 287.71,\n",
-       "  ....O...|.O......|OOOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 74.37,\n",
-       "  OOOOOOOO|OO......|O.......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 4 fitness: 279.88,\n",
-       "  ....O...|OO......|OOOOO...|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 173.85,\n",
-       "  .......O|OOOOOOOO|..O.....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 261.04,\n",
-       "  .......O|.......O|.OOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 71.33,\n",
-       "  ...O....|.......O|OOOOOOOO|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 17 fitness: 5.48,\n",
-       "  .....O..|OOOOOOOO|.....O..|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 20 fitness: 4.97,\n",
-       "  .......O|....O...|.OOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 95.47,\n",
-       "  ...O....|..O.....|.OOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 119.03,\n",
-       "  ...O....|O.......|OOOOOOOO|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|....OOO. x 9 fitness: 331.03,\n",
-       "  ...O....|O.......|....O...|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 74.38,\n",
-       "  ...O....|O.......|.OOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 118.94,\n",
-       "  ...O....|......O.|OOOOOOOO|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 10 fitness: 11.29,\n",
-       "  OOOOOOOO|OO......|..O.....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 315.15,\n",
-       "  ......O.|OO......|..O.....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 81.66,\n",
-       "  ......O.|OO......|OOOOO...|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 5 fitness: 213.84,\n",
-       "  .......O|OOOOOOOO|.....O..|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 20 fitness: 5.04,\n",
-       "  O.......|.O......|OOOOOOOO|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 3 fitness: 253.71,\n",
-       "  O.......|.O......|.OOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 150.37,\n",
-       "  ...O....|..O.....|OOOOOOOO|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 245.42,\n",
-       "  ......O.|.......O|.O......|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 4 fitness: 94.53,\n",
-       "  ......O.|...OOOOO|.O......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 214.97,\n",
-       "  ....O...|.....O..|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 67.20,\n",
-       "  ....O...|OOOOOOOO|.O......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 5 fitness: 248.61,\n",
-       "  ....O...|.....O..|.OOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 85.97,\n",
-       "  ....O...|...OOOOO|.O......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 4 fitness: 207.47,\n",
-       "  ..O.....|.......O|OOOOOOOO|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 18 fitness: 3.02,\n",
-       "  ....O...|......O.|.OOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 98.64,\n",
-       "  .......O|.....O..|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 55.78,\n",
-       "  OOOOOOOO|.......O|.....O..|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 8.03,\n",
-       "  OOOOOOOO|..O.....|...O....|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 307.39,\n",
-       "  ....O...|.....O..|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 56.91,\n",
-       "  ....O...|OOOOOOOO|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 182.62,\n",
-       "  OO......|O.......|OOOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 4 fitness: 310.46,\n",
-       "  ......O.|....O...|.....O..|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 65.52,\n",
-       "  .O......|...O....|.OOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 119.04,\n",
-       "  ......O.|OOOOOOOO|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 106.09,\n",
-       "  OOOOOOOO|OO......|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 292.24,\n",
-       "  ......O.|OO......|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 64.93,\n",
-       "  ......O.|OO......|.O......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 61.84,\n",
-       "  .....O..|OOOOOOOO|......OO|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 19 fitness: 0.31,\n",
-       "  ......O.|.....O..|OOOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 103.05,\n",
-       "  ......O.|OOOOOOOO|..O.....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 121.88,\n",
-       "  OOOOOOOO|..O.....|O.......|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 247.46,\n",
-       "  .......O|.O......|.OOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 92.52,\n",
-       "  OOOOOOOO|...O....|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 252.50,\n",
-       "  .....O..|...O....|.OOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 106.76,\n",
-       "  .O......|..O.....|OOOOOOOO|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 4 fitness: 243.99,\n",
-       "  O.......|.......O|OOOOOOOO|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 18 fitness: 2.74,\n",
-       "  ....O...|..O.....|.OOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 70.05,\n",
-       "  ...O....|O.......|.....O..|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 4 fitness: 87.26,\n",
-       "  .......O|OOOOOOOO|.......O|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 17 fitness: 5.87,\n",
-       "  ..O.....|...O....|......O.|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 76.10,\n",
-       "  ..O.....|.......O|.O......|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 41.86,\n",
-       "  .O......|.....O..|OOOOOOOO|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 8.25,\n",
-       "  ......O.|.....O..|OO......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 33.55,\n",
-       "  ......O.|OOOOOOOO|.OOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 98.97,\n",
-       "  ..O.....|.....O..|OOOOOOOO|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 2 fitness: 3.51,\n",
-       "  .......O|.....O..|O.......|OOOOO...\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 4 fitness: 153.78,\n",
-       "  ..O.....|.O......|.....O..|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 52.00,\n",
-       "  .O......|..O.....|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 75.20,\n",
-       "  OO......|....O...|OOOOOOOO|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 20 fitness: 0.23,\n",
-       "  O.......|.O......|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 91.13,\n",
-       "  ....OOOO|...OOOOO|..O.....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 20 fitness: 403.91,\n",
-       "  ....O...|OOOOOOOO|O.......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 231.92,\n",
-       "  .O......|.O......|.....O..|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 3 fitness: 75.87,\n",
-       "  .O......|.O......|.....O..|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 95.10,\n",
-       "  OOOOOOOO|....O...|OOOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 130.09,\n",
-       "  O.......|......O.|.O......|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 74.98,\n",
-       "  ....O...|....O...|O.......|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 4 fitness: 75.78,\n",
-       "  OOOOOOOO|....O...|O.......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 74.27,\n",
-       "  O.......|...O....|.......O|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 4 fitness: 64.21,\n",
-       "  ...O....|O.......|......O.|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 7 fitness: 87.52,\n",
-       "  ...O....|OO......|......O.|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 63.27,\n",
-       "  ...O....|O.......|......O.|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 138.37,\n",
-       "  .....O..|.....O..|O.......|OOOOO...\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 13 fitness: 164.65,\n",
-       "  OOOOOOOO|....O...|..OOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 63.94,\n",
-       "  ..O.....|..O.....|......O.|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 58.21,\n",
-       "  ......O.|......O.|.O......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 106.69,\n",
-       "  .....O..|......O.|..O.....|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 65.95,\n",
-       "  .....OOO|OOOOOOOO|O.......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|....OOO. x 13 fitness: 252.11,\n",
-       "  .......O|.....O..|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 10 fitness: 95.69,\n",
-       "  .....OOO|OOOOOOOO|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|....OOO. x 14 fitness: 306.69,\n",
-       "  ..O.....|..O.....|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 77.95,\n",
-       "  ...O....|O.......|......O.|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 57.85,\n",
-       "  OOOOOOOO|.......O|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 118.91,\n",
-       "  ....O...|....O...|..O.....|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 85.50,\n",
-       "  .......O|.......O|.O......|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 110.05,\n",
-       "  ....O...|......O.|OO......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 124.79,\n",
-       "  O.......|...O....|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 3 fitness: 90.73,\n",
-       "  ...O....|...O....|......O.|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 3 fitness: 80.99,\n",
-       "  ...O....|...O....|......O.|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 86.22,\n",
-       "  ..O.....|O.......|.....O..|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|....OOO. x 1 fitness: 65.09,\n",
-       "  ....O...|......O.|O.......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 13 fitness: 89.05,\n",
-       "  .......O|..O.....|....O...|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 78.04,\n",
-       "  ......O.|OOOOOOOO|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 246.86,\n",
-       "  O.......|.O......|.....O..|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|....OOOO x 10 fitness: 95.30,\n",
-       "  ......O.|.....O..|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 5 fitness: 92.06,\n",
-       "  ...O....|.....O..|..O.....|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 53.06,\n",
-       "  .....O..|....O...|O.......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 9 fitness: 93.29,\n",
-       "  .O......|O.......|.......O|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 73.90,\n",
-       "  .O......|O.......|.......O|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 7 fitness: 105.39,\n",
-       "  .....O..|....O...|.O......|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 5 fitness: 90.55,\n",
-       "  .....O..|....O...|OO......|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 7 fitness: 157.60,\n",
-       "  .....OOO|....O...|.O......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|....OOO. x 1 fitness: 105.69,\n",
-       "  O.......|O.......|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 72.49,\n",
-       "  .O......|.......O|.O......|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 71.12,\n",
-       "  ......O.|......O.|O.......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 12 fitness: 88.48,\n",
-       "  .....O..|OOOOOOOO|....O...|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 18 fitness: 5.09,\n",
-       "  .O......|OO......|......O.|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 61.60,\n",
-       "  ......O.|OOOOOOO.|.O......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 308.99,\n",
-       "  ...O....|......O.|.O......|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 97.50,\n",
-       "  .O......|...O....|.OOOOOOO|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.OOOOOO. x 14 fitness: 76.35,\n",
-       "  .O......|...O....|.....O..|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 5 fitness: 94.72,\n",
-       "  .O......|...O....|.....O..|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 3 fitness: 74.50,\n",
-       "  .......O|O.......|.O......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 69.50,\n",
-       "  .O......|.O......|OOOOOOOO|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 20 fitness: 397.78,\n",
-       "  .....O..|..O.....|..O.....|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 10 fitness: 96.87,\n",
-       "  ...O....|.......O|.O......|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 65.69,\n",
-       "  .......O|OOOOOOOO|....O...|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 15 fitness: 16.33,\n",
-       "  ....O...|.O......|.O......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 44.48,\n",
-       "  .......O|...O....|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 73.72,\n",
-       "  ....O...|.....O..|O.......|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 13 fitness: 154.56,\n",
-       "  ....O...|.....O..|O.......|OOOOO...\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 4 fitness: 189.98,\n",
-       "  .....OOO|......O.|.O......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|....OOO. x 1 fitness: 107.05,\n",
-       "  .......O|OOOOOOOO|.O......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|....OOOO x 10 fitness: 337.70,\n",
-       "  ....O...|.O......|O.......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 39.59,\n",
-       "  ....O...|......O.|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 87.85,\n",
-       "  .......O|OOOOOOOO|O.......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|....OOOO x 6 fitness: 338.25,\n",
-       "  .O......|O.......|......O.|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 74.06,\n",
-       "  ....O...|....O...|.O......|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 3 fitness: 63.19,\n",
-       "  ..O.....|.O......|......O.|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 82.90,\n",
-       "  ...O....|O.......|OOOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 3 fitness: 298.99,\n",
-       "  ..OOOOOO|...OOOOO|.O......|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 13 fitness: 263.13,\n",
-       "  .O......|...O....|....O...|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 44.24,\n",
-       "  .O......|...O....|....OOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 4 fitness: 144.46,\n",
-       "  ......O.|......O.|...O....|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 101.63,\n",
-       "  ..O.....|...O....|....O...|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 53.85,\n",
-       "  ..O.....|...O....|....OOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 10 fitness: 175.40,\n",
-       "  ......O.|....O...|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 102.23,\n",
-       "  ......O.|....O...|...O....|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 3 fitness: 66.98,\n",
-       "  O.......|...O....|....O...|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 56.22,\n",
-       "  ......O.|......O.|..O.....|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 3 fitness: 105.87,\n",
-       "  .....O..|.......O|.O......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 76.53,\n",
-       "  .....O..|.......O|.O......|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 129.51,\n",
-       "  ....O...|..O.....|..O.....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 8 fitness: 92.74,\n",
-       "  .....O..|.......O|O.......|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 5 fitness: 107.51,\n",
-       "  ......O.|..O.....|.....O..|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 58.86,\n",
-       "  .O......|......O.|.O......|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 98.57,\n",
-       "  .....O..|.......O|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 103.14,\n",
-       "  ...O....|.O......|....O...|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 5 fitness: 98.17,\n",
-       "  ...O....|.OOO....|.OOOOOOO|.OOOO...\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 15 fitness: 463.53,\n",
-       "  .....O..|....O...|....O...|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 66.49,\n",
-       "  ......O.|.......O|O.......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 78.34,\n",
-       "  OOOOOOOO|O.......|.O......|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 7 fitness: 261.87,\n",
-       "  ....O...|......O.|..O.....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 90.39,\n",
-       "  O.......|.....O..|..O.....|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 75.65,\n",
-       "  .O......|.O......|......O.|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 65.25,\n",
-       "  .....OOO|...OOOOO|.O......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 15 fitness: 305.25,\n",
-       "  ..O.....|O.......|OOOOOOOO|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|....OOO. x 2 fitness: 179.30,\n",
-       "  .O......|.O......|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 67.48,\n",
-       "  OOO.....|.....O..|OOOOOO..|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 18 fitness: 0.08,\n",
-       "  .....O..|......O.|...O....|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 108.06,\n",
-       "  ..O.....|.O......|....O...|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 99.83,\n",
-       "  .O......|O.......|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 72.00,\n",
-       "  OOOOOOOO|..O.....|.O......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 4 fitness: 337.29,\n",
-       "  ...O....|.O......|.....O..|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|....OOOO x 2 fitness: 75.69,\n",
-       "  ....O...|..O.....|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 80.81,\n",
-       "  .....O..|...O....|.......O|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 77.70,\n",
-       "  .......O|..O.....|..O.....|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 83.98,\n",
-       "  .......O|..O.....|..O.....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 5 fitness: 92.29,\n",
-       "  .......O|.....O..|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 150.44,\n",
-       "  .......O|...O....|..O.....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 125.54,\n",
-       "  .......O|..O.....|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 149.69,\n",
-       "  ..O.....|.O......|.....O..|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 66.84,\n",
-       "  .......O|.O......|..O.....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 118.98,\n",
-       "  ....O...|.O......|..O.....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 115.18,\n",
-       "  ......O.|......O.|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 135.24,\n",
-       "  .....O..|...O....|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 130.22,\n",
-       "  O.......|.......O|......O.|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 18.15,\n",
-       "  ....O...|..O.....|....O...|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 78.24,\n",
-       "  .......O|......O.|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 152.26,\n",
-       "  ....O...|.O......|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 132.72,\n",
-       "  ......O.|.....O..|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 127.00,\n",
-       "  ......O.|.....O..|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 118.32,\n",
-       "  ......O.|.O......|..O.....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 123.06,\n",
-       "  ....O...|......O.|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 132.15,\n",
-       "  ....O...|......O.|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 65.21,\n",
-       "  .......O|...O....|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 121.09,\n",
-       "  ...O....|..O.....|......O.|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 129.87,\n",
-       "  ...O....|..O.....|......O.|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 4 fitness: 113.37,\n",
-       "  O.......|.O......|.....O..|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 128.21,\n",
-       "  ..O.....|.O......|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 134.23,\n",
-       "  ...O....|...O....|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 126.51,\n",
-       "  O.......|.O......|.......O|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 116.08,\n",
-       "  ......O.|O.......|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 130.84,\n",
-       "  ....O...|.O......|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 132.34,\n",
-       "  ....O...|.O......|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 50.38,\n",
-       "  ...O....|.O......|......O.|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 4 fitness: 121.61,\n",
-       "  ...O....|.O......|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 140.54,\n",
-       "  ...O....|.O......|O.......|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 3 fitness: 142.84,\n",
-       "  ....O...|O.......|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 71.35,\n",
-       "  ......O.|....O...|..O.....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 108.72,\n",
-       "  .O......|O.......|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 122.31,\n",
-       "  ..O.....|...O....|..O.....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 3 fitness: 138.93,\n",
-       "  ..O.....|...O....|OOOOOOOO|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OO. x 13 fitness: 327.83,\n",
-       "  ......O.|..O.....|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 122.20,\n",
-       "  O.......|.O......|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 116.38,\n",
-       "  ....O...|...O....|..O.....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 128.68,\n",
-       "  ...O....|..O.....|....O...|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 67.20,\n",
-       "  .O......|.O......|......O.|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 145.05,\n",
-       "  ..O.....|..O.....|....O...|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 2 fitness: 71.04,\n",
-       "  .....O..|...O....|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 122.15,\n",
-       "  .....O..|...O....|.O......|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 6 fitness: 146.53,\n",
-       "  ....O...|.....O..|.O......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 88.92,\n",
-       "  O.......|OOOO....|OOOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 18 fitness: 436.37,\n",
-       "  ......O.|.....O..|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 114.20,\n",
-       "  .O......|.O......|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 141.47,\n",
-       "  ...O....|O.......|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 125.49,\n",
-       "  ....O...|O.......|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 133.88,\n",
-       "  ....O...|O.......|.O......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 89.25,\n",
-       "  O.......|...O....|O.......|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 11 fitness: 145.35,\n",
-       "  .O......|.O......|.......O|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 149.87,\n",
-       "  ...O....|......O.|.......O|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 22.01,\n",
-       "  ...O....|..O.....|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 3 fitness: 134.80,\n",
-       "  .......O|..O.....|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 2 fitness: 69.24,\n",
-       "  .....O..|O.......|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 99.90,\n",
-       "  ...O....|..O.....|.....O..|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 133.28,\n",
-       "  ....O...|.......O|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 160.27,\n",
-       "  ....O...|.......O|O.......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 90.35,\n",
-       "  ..O.....|..O.....|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 3 fitness: 119.41,\n",
-       "  ...O....|...O....|.....O..|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 128.11,\n",
-       "  .O......|...O....|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 3 fitness: 121.74,\n",
-       "  ....O...|...O....|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 123.83,\n",
-       "  ....O...|...O....|.O......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 90.41,\n",
-       "  .O......|...O....|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 126.91,\n",
-       "  ....O...|......O.|O.......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 64.79,\n",
-       "  ....O...|......O.|O.......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 83.90,\n",
-       "  ..O.....|.O......|.O......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 76.23,\n",
-       "  ......O.|...O....|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 132.65,\n",
-       "  O.......|.O......|......O.|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 116.68,\n",
-       "  .O......|..O.....|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 83.40,\n",
-       "  .....O..|....O...|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 49.86,\n",
-       "  O.......|.O......|....O...|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 116.78,\n",
-       "  ....O...|OOOOOOOO|...O....|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 20 fitness: 554.95,\n",
-       "  .......O|..O.....|.....O..|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 90.29,\n",
-       "  ...O....|..O.....|.......O|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 131.54,\n",
-       "  .......O|.......O|..O.....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 120.17,\n",
-       "  .....O..|..O.....|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 120.49,\n",
-       "  ...O....|...O....|.......O|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 126.17,\n",
-       "  ...O....|...O....|.......O|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 74.56,\n",
-       "  ....O...|...O....|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 142.71,\n",
-       "  ..O.....|...O....|.......O|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 134.73,\n",
-       "  ..O.....|...O....|.......O|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 88.16,\n",
-       "  ...O....|...O....|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 130.48,\n",
-       "  .....O..|.O......|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 108.73,\n",
-       "  .....O..|..O.....|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 135.02,\n",
-       "  ......OO|O.......|.O......|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 3 fitness: 127.22,\n",
-       "  O.......|...O....|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 111.19,\n",
-       "  ......O.|.O......|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 119.27,\n",
-       "  ....O...|....O...|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 3 fitness: 140.13,\n",
-       "  .......O|..O.....|..O.....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 120.24,\n",
-       "  .O......|.O......|......OO|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 127.94,\n",
-       "  OOOOOOO.|O.......|....O...|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......O. x 4 fitness: 110.10,\n",
-       "  ...O....|.O......|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 133.93,\n",
-       "  .......O|O.......|.O......|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 117.09,\n",
-       "  O.......|.O......|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 133.60,\n",
-       "  .....O..|O.......|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 135.36,\n",
-       "  .....OOO|O.......|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|....OOO. x 1 fitness: 150.94,\n",
-       "  ..O.....|...O....|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 137.33,\n",
-       "  .......O|......O.|..O.....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 120.42,\n",
-       "  .O......|..O.....|....O...|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 3 fitness: 67.48,\n",
-       "  .....OOO|...O....|O.......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|....OOO. x 1 fitness: 135.70,\n",
-       "  .O......|..O.....|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 115.14,\n",
-       "  .....OOO|....O...|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|....OOO. x 1 fitness: 165.06,\n",
-       "  ......O.|O.......|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 113.94,\n",
-       "  ..O.....|.O......|......OO|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 121.85,\n",
-       "  ....O...|O.......|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 137.92,\n",
-       "  .....OOO|....O...|O.......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|....OOO. x 1 fitness: 128.92,\n",
-       "  ....O...|....O...|O.......|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 4 fitness: 138.47,\n",
-       "  .....OOO|.O......|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|....OOO. x 1 fitness: 165.07,\n",
-       "  .....O..|.....O..|.O......|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 3 fitness: 118.92,\n",
-       "  .....OOO|.......O|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|....OOO. x 1 fitness: 165.08,\n",
-       "  .O......|..O.....|OOOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 2 fitness: 174.46,\n",
-       "  .....OOO|..O.....|O.......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|....OOO. x 1 fitness: 142.85,\n",
-       "  OOOOOOOO|....O...|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 70.38,\n",
-       "  ..O.....|O.......|OOOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 2 fitness: 226.00,\n",
-       "  .....OOO|..O.....|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|....OOO. x 1 fitness: 167.26,\n",
-       "  .O......|..O.....|OOOOOOO.|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 18 fitness: 261.59,\n",
-       "  .O......|O.......|OOOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 3 fitness: 229.72,\n",
-       "  ..O.....|O.......|......OO|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OO. x 6 fitness: 110.52,\n",
-       "  ....O...|.O......|O.......|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 7 fitness: 133.72,\n",
-       "  .....OOO|O.......|O.......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|....OOO. x 1 fitness: 142.86,\n",
-       "  ......O.|...O....|...O....|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OO. x 1 fitness: 80.56,\n",
-       "  OOOOOOOO|...O....|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 192.19,\n",
-       "  ......OO|....O...|....O...|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 22.87,\n",
-       "  .....O..|......O.|.O......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 87.88,\n",
-       "  .....O..|......O.|.O......|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 8 fitness: 141.47,\n",
-       "  ......O.|OOOOOOOO|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 177.79,\n",
-       "  ..O.....|.O......|OOOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 162.09,\n",
-       "  ......O.|...O....|O.......|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 7 fitness: 132.18,\n",
-       "  OOOOOOOO|OOOO....|O.......|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 7 fitness: 357.46,\n",
-       "  OOOOOOOO|O.......|..O.....|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 6 fitness: 337.87,\n",
-       "  OOOOOOOO|OOOO....|..O.....|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 3 fitness: 383.25,\n",
-       "  ..O.....|OOOO....|OOOOOOOO|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 3 fitness: 369.53,\n",
-       "  ....O...|..O.....|O.......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 3 fitness: 73.73,\n",
-       "  ....O...|OOOO....|OOOOOOOO|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 166.70,\n",
-       "  ..O.....|.O......|OOOOOOOO|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOO. x 11 fitness: 91.15,\n",
-       "  .....O..|OOOOOOOO|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 2 fitness: 173.17,\n",
-       "  .....O..|....O...|.O......|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 121.78,\n",
-       "  ..O.....|O.......|.....OOO|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 3 fitness: 188.78,\n",
-       "  ..OOOOOO|.O......|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 90.82,\n",
-       "  ..O.....|.O......|.......O|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 46.48,\n",
-       "  ....O...|.....O..|O.......|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 149.43,\n",
-       "  ....O...|OOOO....|...O....|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 144.43,\n",
-       "  .....OOO|.O......|OOOO....|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 18 fitness: 310.93,\n",
-       "  ....O...|OOOO....|.O......|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 145.59,\n",
-       "  .....O..|.....O..|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 71.26,\n",
-       "  ..O.....|O.......|.......O|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 4 fitness: 70.98,\n",
-       "  ..O.....|O.......|.......O|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 3 fitness: 101.99,\n",
-       "  ....O...|...OOOOO|.O......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 13 fitness: 237.01,\n",
-       "  ..O.....|...O....|....O...|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 57.27,\n",
-       "  ......O.|.O......|O.......|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 114.84,\n",
-       "  ......O.|....O...|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 2 fitness: 47.16,\n",
-       "  ....O...|OOOO....|..O.....|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 128.72,\n",
-       "  .....O..|...O....|...O....|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 63.43,\n",
-       "  ..O.....|...O....|.....O..|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OO. x 1 fitness: 68.47,\n",
-       "  .......O|OOOOOOOO|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 13 fitness: 230.18,\n",
-       "  .....OOO|.......O|...O....|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 12 fitness: 175.31,\n",
-       "  .....O..|.O......|O.......|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 127.72,\n",
-       "  .....OOO|.......O|.O......|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 13 fitness: 185.62,\n",
-       "  OOOOOOOO|...O....|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 307.31,\n",
-       "  .O......|.O......|....O...|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 101.83,\n",
-       "  ...OOOOO|..O.....|OO......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 16 fitness: 278.09,\n",
-       "  ..O.....|..O.....|OOOOOOOO|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 9 fitness: 263.94,\n",
-       "  .OOOO...|.O......|.....O..|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 100.72,\n",
-       "  .O......|...O....|O.......|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 123.77,\n",
-       "  ..O.....|O.......|OOOOOOOO|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 247.62,\n",
-       "  ......O.|.....O..|OOOOOO..|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 3 fitness: 219.56,\n",
-       "  .....O..|.......O|O.......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 58.08,\n",
-       "  .....O..|....O...|OO......|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 118.53,\n",
-       "  ...O....|...O....|O.......|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 3 fitness: 127.40,\n",
-       "  .......O|......O.|...O....|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 4 fitness: 98.58,\n",
-       "  ......O.|...O....|O.......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 3 fitness: 69.39,\n",
-       "  .O......|O.......|..O.....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 81.09,\n",
-       "  ....O...|.O......|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 78.43,\n",
-       "  ..O.....|...O....|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 68.48,\n",
-       "  ......O.|...O....|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 2 fitness: 76.52,\n",
-       "  ..O.....|......O.|...O....|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 57.74,\n",
-       "  ......O.|.....O..|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 3 fitness: 81.19,\n",
-       "  ...O....|O.......|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 67.07,\n",
-       "  ...O....|O.......|.......O|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 58.54,\n",
-       "  ..O.....|.O......|O.......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 72.41,\n",
-       "  ..O.....|.O......|O.......|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 56.97,\n",
-       "  .....O..|......O.|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 2 fitness: 65.82,\n",
-       "  .....O..|......O.|...O....|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 4 fitness: 71.02,\n",
-       "  .O......|..O.....|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 77.42,\n",
-       "  ..O.....|O.......|.O......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 83.30,\n",
-       "  .O......|...O....|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 2 fitness: 73.54,\n",
-       "  .....O..|O.......|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 4 fitness: 77.35,\n",
-       "  ..O.....|O.......|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 83.30,\n",
-       "  .O......|..O.....|.....O..|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 3 fitness: 73.58,\n",
-       "  ..O.....|.O......|......O.|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 67.54,\n",
-       "  ..O.....|.O......|......OO|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 99.72,\n",
-       "  ..O.....|.O......|OOOOOOOO|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 93.77,\n",
-       "  .......O|....O...|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 44.92,\n",
-       "  .......O|OOOOOO..|....O...|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 2 fitness: 11.72,\n",
-       "  ...O....|..O.....|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 58.67,\n",
-       "  .O......|O.......|....O...|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 3 fitness: 85.81,\n",
-       "  .O......|O.......|.O......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 85.81,\n",
-       "  ......O.|......O.|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 72.49,\n",
-       "  ......O.|......O.|...O....|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 4 fitness: 63.80,\n",
-       "  ..O.....|..O.....|.....O..|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 43.99,\n",
-       "  O.......|.O......|OOOOO...|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 16 fitness: 244.72,\n",
-       "  ..O.....|.O......|..O.....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 57.51,\n",
-       "  .O......|..O.....|.......O|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 64.95,\n",
-       "  ..O.....|..O.....|......O.|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 66.96,\n",
-       "  ..O.....|..O.....|....O...|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 56.17,\n",
-       "  ..O.....|..O.....|....O...|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 47.62,\n",
-       "  .O......|O.......|.......O|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 90.57,\n",
-       "  .O......|...O....|......O.|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 58.01,\n",
-       "  ......O.|.......O|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 68.45,\n",
-       "  .O......|...O....|O.......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 58.01,\n",
-       "  .O......|..O.....|....O...|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 65.28,\n",
-       "  ...O....|...O....|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 72.21,\n",
-       "  .....O..|..O.....|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 3 fitness: 74.67,\n",
-       "  .O......|...O....|.....O..|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 69.53,\n",
-       "  ......O.|.......O|O.......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 3 fitness: 67.50,\n",
-       "  .......O|...O....|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 3 fitness: 75.88,\n",
-       "  ..O.....|..O.....|O.......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 66.61,\n",
-       "  ..O.....|O.......|.......O|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 84.60,\n",
-       "  .O......|...O....|.O......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 79.12,\n",
-       "  ..O.....|.O......|.O......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 74.65,\n",
-       "  ..O.....|.O......|.O......|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 96.51,\n",
-       "  ....O...|..O.....|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 65.86,\n",
-       "  .O......|..O.....|......O.|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 60.56,\n",
-       "  .O......|...O....|..O.....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 62.33,\n",
-       "  .....O..|.O......|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 68.39,\n",
-       "  ..O.....|O.......|..O.....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 84.44,\n",
-       "  .O......|...O....|.......O|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 78.35,\n",
-       "  .....O..|......O.|O.......|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 3 fitness: 73.68,\n",
-       "  ......O.|......O.|O.......|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 4 fitness: 107.07,\n",
-       "  ......O.|...OOOOO|...O....|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 3 fitness: 170.30,\n",
-       "  ...O....|......O.|...O....|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 72.35,\n",
-       "  .O......|O.......|......O.|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 82.02,\n",
-       "  ....O...|.......O|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 70.80,\n",
-       "  .O......|..O.....|..O.....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 3 fitness: 67.37,\n",
-       "  .....O..|.....O..|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 62.45,\n",
-       "  O.......|...O....|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 67.85,\n",
-       "  ...O....|.O......|OO......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 6 fitness: 104.96,\n",
-       "  .....O..|....O...|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 2 fitness: 50.77,\n",
-       "  .O......|..O.....|.O......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 2 fitness: 62.29,\n",
-       "  ..O.....|.O......|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 59.94,\n",
-       "  OOOOOOO.|OOOO....|.OO.....|.OOOO...\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 15 fitness: 521.11,\n",
-       "  ..O.....|O.......|.....O..|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 2 fitness: 81.11,\n",
-       "  ..O.....|O.......|.....O..|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 63.30,\n",
-       "  ..O.....|O.......|O.......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 81.36,\n",
-       "  ....O...|.......O|O.......|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 8 fitness: 141.70,\n",
-       "  ...OOOOO|O.......|...O....|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 9 fitness: 224.09,\n",
-       "  ......O.|.O......|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 63.75,\n",
-       "  ......O.|O.......|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 69.67,\n",
-       "  ...O....|.O......|....O...|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 4 fitness: 101.15,\n",
-       "  ....O...|.......O|O.......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 5 fitness: 85.67,\n",
-       "  ....O...|...O....|O.......|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 11 fitness: 143.61,\n",
-       "  ..O.....|..O.....|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 61.64,\n",
-       "  .O......|...O....|....O...|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 60.97,\n",
-       "  ...OOOO.|.......O|O.......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 109.39,\n",
-       "  .O......|O.......|.....O..|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 83.21,\n",
-       "  ....O...|..O.....|O.......|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 102.84,\n",
-       "  OOOOOOO.|OOOO....|.OO.....|..OO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 185.05,\n",
-       "  ..O.....|O.......|......O.|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 82.11,\n",
-       "  ...OOOOO|.......O|O.......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 138.39,\n",
-       "  ......O.|.......O|O.......|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 58.47,\n",
-       "  .O......|O.......|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|...OOOOO x 1 fitness: 83.54,\n",
-       "  O.......|...O....|.....O..|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OO. x 1 fitness: 63.51,\n",
-       "  OO......|.O......|.......O|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......O. x 10 fitness: 123.48,\n",
-       "  .......O|......O.|...O....|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 3 fitness: 74.94,\n",
-       "  ...O....|...O....|OOOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 6 fitness: 238.07,\n",
-       "  OOOOOOOO|...O....|O.......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 4 fitness: 237.42,\n",
-       "  O.......|.O......|....O...|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 93.60,\n",
-       "  O.......|.O......|OOOOO...|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 196.57,\n",
-       "  OOOOOOO.|O.......|O.......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 4 fitness: 219.08,\n",
-       "  .....O..|.....O..|OOOOOO..|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 9 fitness: 174.16,\n",
-       "  ...O....|...O....|......OO|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 78.99,\n",
-       "  OOOOOOO.|O.......|......O.|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 164.48,\n",
-       "  OOOOOOO.|O.......|.......O|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 3 fitness: 176.96,\n",
-       "  ...O....|..O.....|.....O..|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......O. x 9 fitness: 64.01,\n",
-       "  OOOOOOO.|O.......|....O...|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 109.66,\n",
-       "  ...O....|O.......|O.......|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 165.34,\n",
-       "  ...O....|...O....|.......O|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 56.33,\n",
-       "  .....O..|O.......|O.......|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 158.16,\n",
-       "  O.......|.O......|OOOOOOOO|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 276.23,\n",
-       "  .....O..|.......O|O.......|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......O. x 1 fitness: 44.11,\n",
-       "  .....O..|.......O|O.......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 38.30,\n",
-       "  ....O...|......O.|O.......|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 114.94,\n",
-       "  ....O...|O.......|O.......|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 163.60,\n",
-       "  O.......|...O....|OOOOOOOO|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 9 fitness: 292.87,\n",
-       "  ...O....|.O......|.....O..|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......O. x 2 fitness: 42.95,\n",
-       "  ...O....|.O......|OOOOOOOO|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 11 fitness: 343.70,\n",
-       "  ...O....|.O......|.....O..|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 53.18,\n",
-       "  ...O....|O.......|......OO|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 5 fitness: 103.61,\n",
-       "  ...O....|...O....|.....O..|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OO. x 2 fitness: 76.00,\n",
-       "  O.......|O.......|O.......|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 162.34,\n",
-       "  ....O...|.....O..|O.......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......O. x 1 fitness: 57.61,\n",
-       "  .OOOOO..|O.......|....O...|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 105.78,\n",
-       "  ..O.....|O.......|....O...|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......O. x 1 fitness: 42.61,\n",
-       "  ...O....|O.......|....O...|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 148.66,\n",
-       "  .OOOOO..|O.......|.O......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 151.69,\n",
-       "  .OOOOO..|O.......|.......O|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 140.53,\n",
-       "  OOO.....|O.......|.....OOO|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 10 fitness: 258.64,\n",
-       "  .OOOOO..|O.......|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 150.24,\n",
-       "  OO......|O.......|OOOOOOO.|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 15 fitness: 367.29,\n",
-       "  ...O....|O.......|.....O..|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 4 fitness: 164.25,\n",
-       "  O.......|O.......|.......O|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 78.71,\n",
-       "  O.......|...O....|..O.....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 93.16,\n",
-       "  ......O.|...OOOOO|...O....|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 4 fitness: 260.92,\n",
-       "  O.......|..O.....|..O.....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 96.48,\n",
-       "  .OOOOO..|O.......|OO......|.OO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 3 fitness: 300.14,\n",
-       "  ...O....|O.......|.......O|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 161.77,\n",
-       "  .......O|OOOOOOOO|..O.....|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 280.24,\n",
-       "  .....O..|...OOOOO|...O....|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 16 fitness: 268.26,\n",
-       "  ...O....|...O....|.OOOOOOO|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 10 fitness: 303.04,\n",
-       "  ...O....|...O....|OOOOOOOO|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 4 fitness: 321.58,\n",
-       "  ...O....|...O....|....O...|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 68.25,\n",
-       "  .......O|O.......|...O....|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 62.26,\n",
-       "  ......O.|O.......|O.......|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 5 fitness: 156.62,\n",
-       "  .......O|O.......|O.......|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 3 fitness: 158.17,\n",
-       "  ...O....|..O.....|OOOOOOOO|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 6 fitness: 320.12,\n",
-       "  OOO.....|..O.....|OOOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 19 fitness: 403.38,\n",
-       "  ...O....|.O......|......O.|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 103.27,\n",
-       "  O.......|.O......|......O.|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 100.16,\n",
-       "  ....O...|OOOOOOOO|.O......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 6 fitness: 330.58,\n",
-       "  ....O...|.O......|O.......|OOOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 97.70,\n",
-       "  ......OO|O.......|O.......|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 3 fitness: 114.46,\n",
-       "  ...O....|O.......|..O.....|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 146.77,\n",
-       "  O.......|.O......|......OO|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 60.30,\n",
-       "  OOOOOOOO|..O.....|..O.....|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 268.92,\n",
-       "  .......O|..O.....|..O.....|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 62.04,\n",
-       "  ..O.....|...O....|OOOOOOOO|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|....OOOO x 19 fitness: 326.66,\n",
-       "  .......O|.O......|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 83.67,\n",
-       "  ....O...|...O....|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 111.04,\n",
-       "  ...O....|O.......|.....O..|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......O. x 2 fitness: 39.20,\n",
-       "  .......O|...O....|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 83.69,\n",
-       "  .......O|......O.|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 83.69,\n",
-       "  ......O.|...O....|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 111.74,\n",
-       "  .....O..|...O....|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 111.75,\n",
-       "  .......O|..O.....|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 83.70,\n",
-       "  ...O....|..O.....|......OO|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 77.52,\n",
-       "  OOOOOOOO|...O....|...O....|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 3 fitness: 311.14,\n",
-       "  ...O....|O.......|...O....|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 144.64,\n",
-       "  .......O|...O....|..O.....|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 91.25,\n",
-       "  OOOOOOOO|...O....|..O.....|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 8 fitness: 316.12,\n",
-       "  O.......|O.......|....O...|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 56.60,\n",
-       "  .......O|.......O|..O.....|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 3 fitness: 96.35,\n",
-       "  O.......|...O....|......O.|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 143.25,\n",
-       "  O.......|.O......|....O...|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 130.65,\n",
-       "  O.......|.O......|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 131.58,\n",
-       "  .......O|....O...|..O.....|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 3 fitness: 100.51,\n",
-       "  .......O|....O...|..O.....|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|....OOOO x 4 fitness: 81.54,\n",
-       "  .......O|......O.|..O.....|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 3 fitness: 99.84,\n",
-       "  O.......|..O.....|......O.|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 143.06,\n",
-       "  O.......|...O....|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 139.18,\n",
-       "  O.......|..O.....|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 147.42,\n",
-       "  O.......|..O.....|.O......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 143.13,\n",
-       "  OOOOOOOO|...O....|OOOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 373.68,\n",
-       "  .O......|O.......|....O...|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 61.67,\n",
-       "  ....O...|....O...|..O.....|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|....OOOO x 3 fitness: 84.67,\n",
-       "  ....O...|OOOOOOOO|..O.....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 164.14,\n",
-       "  ..O.....|O.......|....O...|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 4 fitness: 70.28,\n",
-       "  O.......|...O....|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 139.57,\n",
-       "  O.......|.O......|.......O|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 131.41,\n",
-       "  O.......|O.......|OOOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 229.96,\n",
-       "  O.......|.O......|...O....|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 131.58,\n",
-       "  O.......|..O.....|.......O|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 143.39,\n",
-       "  O.......|...O....|.....O..|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 134.66,\n",
-       "  .....O..|......O.|.O......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......O. x 1 fitness: 37.82,\n",
-       "  .....O..|......O.|.O......|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 67.20,\n",
-       "  ....O...|...O....|..O.....|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|....OOOO x 1 fitness: 62.62,\n",
-       "  OOOOO...|...O....|..O.....|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 94.64,\n",
-       "  ......O.|.....O..|OO......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 3 fitness: 86.64,\n",
-       "  O.......|..O.....|....O...|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 143.55,\n",
-       "  ...O....|.O......|OOOOOOOO|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 10 fitness: 254.28,\n",
-       "  ...O....|O.......|....O...|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 55.93,\n",
-       "  ...O....|OOOOOOOO|......O.|OOOOO...\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 159.86,\n",
-       "  ......O.|....O...|..O.....|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|....OOOO x 5 fitness: 87.21,\n",
-       "  O.......|...O....|....O...|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 134.66,\n",
-       "  O.......|...O....|OOOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 237.46,\n",
-       "  .O......|OOOOOOOO|...O....|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 162.10,\n",
-       "  .......O|...O....|OOOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 232.39,\n",
-       "  ....OOOO|...O....|.O......|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 145.61,\n",
-       "  .O......|...O....|OOOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 237.46,\n",
-       "  OOOOOOOO|...O....|O.......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 239.86,\n",
-       "  ....OOOO|...O....|O.......|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 8 fitness: 174.84,\n",
-       "  ..O.....|...O....|OOOOOOOO|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 239.86,\n",
-       "  OOOOOOOO|...O....|......O.|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 218.44,\n",
-       "  O.......|...O....|OOOOOOO.|OOOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 7 fitness: 275.29,\n",
-       "  O.......|...O....|......OO|OOOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 115.27,\n",
-       "  OOOOOOOO|...O....|.O......|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 231.27,\n",
-       "  .....O..|..O.....|.O......|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 93.75,\n",
-       "  OOOOOOOO|...O....|...O....|.O......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 228.36,\n",
-       "  O.......|.O......|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 127.14,\n",
-       "  ...O....|...O....|......OO|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 79.63,\n",
-       "  .......O|.....O..|..O.....|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 3 fitness: 100.99,\n",
-       "  OOOOOOOO|...O....|....O...|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 86.85,\n",
-       "  O.......|...O....|O.......|.OOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 135.07,\n",
-       "  .O......|.O......|...O....|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 121.47,\n",
-       "  OOOOOOOO|.....O..|..O.....|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 101.65,\n",
-       "  .......O|.......O|.O......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 52.66,\n",
-       "  .....OOO|.......O|.O......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 101.61,\n",
-       "  ......O.|.....O..|OO......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......O. x 1 fitness: 37.01,\n",
-       "  ....O...|.....O..|..O.....|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 3 fitness: 49.65,\n",
-       "  ......O.|...OOOOO|...O....|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 3 fitness: 217.24,\n",
-       "  .......O|.....O..|..O.....|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 47.50,\n",
-       "  ...O....|...O....|....O...|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 42.46,\n",
-       "  OOOO....|...O....|.OOOO...|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 3 fitness: 183.09,\n",
-       "  .O......|O.......|...O....|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 111.34,\n",
-       "  .....O..|.....O..|..O.....|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 47.55,\n",
-       "  .O......|OO......|...O....|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 79.02,\n",
-       "  .......O|OOOOOOOO|O.......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......O. x 3 fitness: 159.23,\n",
-       "  ....O...|OOOOOOOO|O.......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......O. x 1 fitness: 178.86,\n",
-       "  ......O.|......O.|...O....|OOOO....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 59.64,\n",
-       "  O.......|...O....|....O...|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 40.61,\n",
-       "  O.......|...O....|OOOOOOO.|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 111.85,\n",
-       "  O.......|O.......|O.......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......O. x 1 fitness: 38.24,\n",
-       "  ...O....|O.......|......O.|OOOOO...\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 101.68,\n",
-       "  ......O.|OOOOOOOO|O.......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......O. x 1 fitness: 164.17,\n",
-       "  ......O.|O.......|O.......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 35.97,\n",
-       "  ......O.|.......O|.O......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 50.34,\n",
-       "  .....OOO|.......O|.O......|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 102.51,\n",
-       "  ...O....|...O....|......O.|OOOOO...\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 92.64,\n",
-       "  ..O.....|.O......|OOOOOOOO|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 112.11,\n",
-       "  ..O.....|O.......|.O......|OOO.....\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 1 fitness: 69.75,\n",
-       "  OOOOOOOO|..O.....|O.......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......O. x 2 fitness: 78.73,\n",
-       "  .......O|.O......|..O.....|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 100.67,\n",
-       "  ....OOOO|...O....|O.......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 68.43,\n",
-       "  ..O.....|..O.....|OOOOOOOO|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 98.10,\n",
-       "  ....OOOO|...O....|..O.....|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 70.48,\n",
-       "  ..O.....|OOOOOOOO|....O...|OO......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 93.40,\n",
-       "  .O......|...O....|....O...|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 35.43,\n",
-       "  ....OOOO|...O....|.O......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 79.83,\n",
-       "  .......O|OOOOOOOO|OOOOOOOO|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 217.19,\n",
-       "  OOOOOOOO|..O.....|OOOOOOOO|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 3 fitness: 254.32,\n",
-       "  OO......|.....O..|OOOOOOOO|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 2.22,\n",
-       "  OOOOOOOO|......O.|......OO|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 121.52,\n",
-       "  OOOOOOOO|..O.....|......OO|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 101.10,\n",
-       "  OOOOOOOO|OOOOOOOO|.O......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 143.69,\n",
-       "  OOOOOOOO|O.......|......OO|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 105.51,\n",
-       "  OOOOOOOO|...O....|......OO|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 47.70,\n",
-       "  .......O|OOOOOOOO|...O....|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 115.46,\n",
-       "  .......O|OOOOOOOO|..O.....|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 122.90,\n",
-       "  .....OOO|OOOOOOOO|OOOOOOOO|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 137.03,\n",
-       "  OOOOOO..|......O.|..O.....|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 71.67,\n",
-       "  .OOOOOOO|.......O|OOOOOOOO|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 24.58,\n",
-       "  OOOOOOOO|OOOOOOOO|O.......|O.......\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 92.70,\n",
-       "  .OOOOOOO|.......O|..O.....|OOOOOOOO\t0\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 25.87,\n",
-       "  OOOOOOOO|....O...|OOOOOOOO|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 158.22,\n",
-       "  OOOOOOOO|OOOOOOOO|......O.|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 177.19,\n",
-       "  O.......|.......O|.O......|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 29.88,\n",
-       "  OOOOOOOO|......O.|OOOOOOOO|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 228.01,\n",
-       "  .......O|OOOOOOOO|OOOOOOOO|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 63.85,\n",
-       "  OOOOOOOO|..O.....|....O...|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 47.03,\n",
-       "  .O......|.......O|....O...|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 34.54,\n",
-       "  ....O...|O.......|.......O|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 41.82,\n",
-       "  ......O.|OOOOOOOO|.O......|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 19 fitness: 4.07,\n",
-       "  ..O.....|.....O..|.....O..|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 44.52,\n",
-       "  ...O....|.......O|...O....|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 42.05,\n",
-       "  ...O....|OOOOOOOO|...O....|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 85.48,\n",
-       "  ..O.....|.....O..|.......O|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 46.04,\n",
-       "  .......O|OOOOOOOO|O.......|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 19 fitness: 4.43,\n",
-       "  .O......|.....O..|.......O|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 45.81,\n",
-       "  O.......|.......O|O.......|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 41.16,\n",
-       "  ......O.|O.......|....O...|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 36.29,\n",
-       "  ......O.|OOOOOOOO|....O...|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 15 fitness: 317.16,\n",
-       "  OOOOOOOO|......O.|...O....|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 57.83,\n",
-       "  .......O|...O....|....O...|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 43.55,\n",
-       "  .......O|OOOOOOOO|....O...|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 99.64,\n",
-       "  ...O....|.......O|.......O|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 48.62,\n",
-       "  ...O....|.......O|OOOOOOOO|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 4 fitness: 154.48,\n",
-       "  O.......|.O......|OOOOOOOO|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 20 fitness: 5.16,\n",
-       "  ....O...|OOOOOOOO|.O......|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 18 fitness: 2.81,\n",
-       "  .......O|.......O|.....O..|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 36.80,\n",
-       "  .......O|OOOOOOOO|.....O..|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 149.62,\n",
-       "  ......O.|...O....|....O...|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 39.29,\n",
-       "  .O......|O.......|.....O..|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 61.33,\n",
-       "  ......O.|OOOOOOOO|O.......|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 20 fitness: 2.95,\n",
-       "  .......O|O.......|.....O..|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 46.40,\n",
-       "  .......O|O.......|......O.|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 40.39,\n",
-       "  OOOOOOOO|O.......|......O.|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 56.23,\n",
-       "  .......O|OOOOOOOO|......O.|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 177.22,\n",
-       "  .......O|.O......|.......O|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 43.68,\n",
-       "  .......O|OOOOOOOO|.......O|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 114.41,\n",
-       "  ..O.....|...O....|OOOOOOOO|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 19 fitness: 3.91,\n",
-       "  .O......|...O....|OOOOOOOO|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 20 fitness: 2.13,\n",
-       "  ...O....|.......O|.....O..|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 35.04,\n",
-       "  ....O...|.......O|.....O..|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 39.62,\n",
-       "  .......O|..O.....|......O.|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 41.41,\n",
-       "  .......O|..O.....|OOOOOOOO|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 29.80,\n",
-       "  .O......|.......O|.O......|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 40.27,\n",
-       "  .O......|......O.|.......O|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 46.22,\n",
-       "  .O......|......O.|OOOOOOOO|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 4 fitness: 235.78,\n",
-       "  ......O.|OOOOOOOO|...O....|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 3 fitness: 10.64,\n",
-       "  .......O|OOOOOOOO|...O....|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 5 fitness: 5.94,\n",
-       "  ...O....|......O.|..O.....|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 42.80,\n",
-       "  ...O....|......O.|OOOOOOOO|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 122.64,\n",
-       "  OOOOOOOO|OOOOOOOO|..O.....|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 105.09,\n",
-       "  ...O....|....O...|...O....|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 55.33,\n",
-       "  ...O....|....O...|OOOOOOOO|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 152.93,\n",
-       "  ..O.....|....O...|......O.|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 35.31,\n",
-       "  ..O.....|....O...|OOOOOOOO|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 180.40,\n",
-       "  OOOOOOOO|....O...|......O.|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 116.04,\n",
-       "  .....O..|OOOOOOOO|O.......|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 19 fitness: 2.25,\n",
-       "  .O......|.......O|...O....|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 24.03,\n",
-       "  ...O....|.....O..|OOOOOOOO|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 147.26,\n",
-       "  ...O....|.....O..|.O......|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 76.33,\n",
-       "  O.......|O.......|OOOOOOOO|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 20 fitness: 5.33,\n",
-       "  .....O..|...O....|.......O|O.......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 74.49,\n",
-       "  .....O..|OOOOOOOO|.......O|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 119.04,\n",
-       "  OOOOOOOO|....O...|....O...|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 190.13,\n",
-       "  ...O....|...O....|OOOOOOOO|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 20 fitness: 2.66,\n",
-       "  OOOOOOOO|....O...|.......O|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 188.83,\n",
-       "  O.......|...O....|OOOOOOOO|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 19 fitness: 3.80,\n",
-       "  .....O..|OOOOOOOO|.....O..|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 172.95,\n",
-       "  .....O..|OOOOOOOO|.O......|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 19 fitness: 4.87,\n",
-       "  OOOOOOOO|......O.|......O.|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 204.14,\n",
-       "  ..O.....|......O.|OOOOOOOO|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 206.23,\n",
-       "  ...O....|OOOOOOOO|..O.....|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 73.61,\n",
-       "  ...O....|.......O|..O.....|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 41.92,\n",
-       "  ...O....|...O....|..O.....|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 92.90,\n",
-       "  .O......|.O......|OOOOOOOO|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 20 fitness: 2.15,\n",
-       "  ......O.|OOOOOOOO|......O.|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 165.76,\n",
-       "  ......O.|OOOOOOOO|......O.|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 15 fitness: 325.18,\n",
-       "  .......O|OOOOOOOO|.O......|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 19 fitness: 5.41,\n",
-       "  OOOOOOOO|......O.|.......O|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 206.93,\n",
-       "  .......O|......O.|OOOOOOOO|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 111.11,\n",
-       "  .O......|......O.|...O....|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 28.44,\n",
-       "  .O......|......O.|OOOOOOOO|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 203.30,\n",
-       "  ...O....|.O......|OOOOOOOO|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 20 fitness: 7.51,\n",
-       "  ....O...|..O.....|.....O..|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 49.67,\n",
-       "  ....O...|OOOOOOOO|.....O..|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 193.73,\n",
-       "  ...O....|O.......|..O.....|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 80.37,\n",
-       "  ...O....|O.......|OOOOOOOO|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 20 fitness: 3.57,\n",
-       "  ....O...|.......O|.....O..|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 68.09,\n",
-       "  ....O...|.......O|OOOOOOOO|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 85.62,\n",
-       "  OOOOOOOO|.......O|.....O..|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 214.64,\n",
-       "  OOOOOOOO|.....O..|......O.|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 148.86,\n",
-       "  ......O.|.....O..|.......O|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 60.01,\n",
-       "  ..O.....|.....O..|.O......|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 15 fitness: 97.23,\n",
-       "  .....O..|OOOOOOOO|...O....|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 3 fitness: 10.40,\n",
-       "  OOOOOOOO|......O.|..O.....|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 147.20,\n",
-       "  OOOOOOOO|.......O|......O.|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 157.05,\n",
-       "  ..O.....|.......O|......O.|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 48.51,\n",
-       "  ..O.....|OOOOOOOO|......O.|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 77.04,\n",
-       "  .O......|OOOOOOOO|......O.|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 111.80,\n",
-       "  ..O.....|.......O|.O......|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 54.46,\n",
-       "  OOOOOOOO|....O...|.....O..|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 160.62,\n",
-       "  .....O..|....O...|.....O..|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 70.77,\n",
-       "  OOOOOOOO|....O...|.....O..|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 5 fitness: 247.31,\n",
-       "  ...O....|.......O|.....O..|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 59.14,\n",
-       "  ....O...|OOOOOOOO|...O....|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 3 fitness: 20.07,\n",
-       "  .O......|....O...|OOOOOOOO|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 175.50,\n",
-       "  .O......|....O...|OOOOOOOO|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 5 fitness: 230.59,\n",
-       "  ..O.....|....O...|O.......|O.......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 76.39,\n",
-       "  ..O.....|....O...|OOOOOOOO|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 194.69,\n",
-       "  ..O.....|....O...|.......O|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 45.29,\n",
-       "  .....O..|OOOOOOOO|....O...|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 188.29,\n",
-       "  .....O..|.O......|....O...|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 47.54,\n",
-       "  O.......|.......O|.......O|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 50.17,\n",
-       "  O.......|.......O|.OOOOOOO|OO......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 18 fitness: 264.68,\n",
-       "  OOOOOOOO|.....O..|...O....|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 57.89,\n",
-       "  O.......|.....O..|.OOOOOOO|OO......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 12 fitness: 280.09,\n",
-       "  ...O....|....O...|OOOOOOOO|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 173.29,\n",
-       "  .....O..|OOOOOOOO|......O.|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 146.64,\n",
-       "  .....O..|...O....|......O.|O.......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 6 fitness: 73.23,\n",
-       "  OOOOOOOO|.O......|......O.|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 131.54,\n",
-       "  ....O...|OOOOOOOO|......O.|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 177.58,\n",
-       "  .....O..|..O.....|....O...|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 16.04,\n",
-       "  .....O..|..O.....|....O...|OO......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 80.79,\n",
-       "  .....O..|..O.....|....O...|O.......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 4 fitness: 68.95,\n",
-       "  ....O...|OOOOOOOO|O.......|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 20 fitness: 5.48,\n",
-       "  OOOOOOOO|......O.|.....O..|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 182.04,\n",
-       "  OOOOOOOO|......O.|.....O..|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 212.11,\n",
-       "  .......O|OOOOOOOO|.....O..|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 216.02,\n",
-       "  .O......|OOOOOOOO|....O...|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 75.62,\n",
-       "  ...O....|......O.|OOOOOOOO|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 214.90,\n",
-       "  ...O....|......O.|..O.....|O.......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 81.37,\n",
-       "  .O......|.......O|OOOOOOOO|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 168.76,\n",
-       "  .....O..|.....O..|....O...|O.......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 79.88,\n",
-       "  ....O...|OOOOOOOO|.......O|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 175.69,\n",
-       "  .O......|.....O..|OOOOOOOO|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 185.07,\n",
-       "  OOOOOOOO|.....O..|.....O..|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 177.47,\n",
-       "  .......O|.......O|OOOOOOOO|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 67.70,\n",
-       "  ..O.....|.......O|OOOOOOOO|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 219.76,\n",
-       "  ..O.....|O.......|OOOOOOOO|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 20 fitness: 7.66,\n",
-       "  .....O..|OOOOOOOO|.....O..|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 212.34,\n",
-       "  OOOOOOOO|.......O|.....O..|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 214.79,\n",
-       "  ....O...|OOOOOOOO|......O.|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 160.07,\n",
-       "  ....O...|O.......|......O.|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 23.21,\n",
-       "  ....O...|..O.....|....O...|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 24.25,\n",
-       "  ....O...|..O.....|....O...|OO......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 3 fitness: 95.19,\n",
-       "  ....O...|OOOOOOOO|....O...|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 153.52,\n",
-       "  ......O.|..O.....|.......O|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 46.57,\n",
-       "  OOOOOOOO|......O.|......O.|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 161.62,\n",
-       "  OOOOOOOO|.O......|...O....|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 5 fitness: 10.73,\n",
-       "  .......O|OOOOOOOO|......O.|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 251.98,\n",
-       "  .O......|OOOOOOOO|.......O|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 74.61,\n",
-       "  OOOOOOOO|......O.|.......O|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 251.85,\n",
-       "  OOOOOOOO|.......O|....O...|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 3 fitness: 257.08,\n",
-       "  .....O..|OOOOOOOO|.......O|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 194.54,\n",
-       "  O.......|......O.|OOOOOOOO|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 151.65,\n",
-       "  ......O.|.....O..|.O......|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 63.93,\n",
-       "  .O......|.......O|OOOOOOOO|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 239.60,\n",
-       "  .O......|.......O|OOOOOOOO|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 5 fitness: 297.94,\n",
-       "  O.......|.......O|OOOOOOOO|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 232.43,\n",
-       "  OOOOOOOO|......O.|....O...|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 150.35,\n",
-       "  .O......|OOOOOOOO|....O...|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 133.18,\n",
-       "  .O......|OOOOOOOO|....O...|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 89.61,\n",
-       "  O.......|.....O..|OOOOOOOO|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 6 fitness: 220.93,\n",
-       "  O.......|....O...|OOOOOOOO|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 140.41,\n",
-       "  OOOOOOOO|O.......|....O...|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 149.24,\n",
-       "  OOOOOOOO|.....O..|O.......|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 78.40,\n",
-       "  OOOOOOOO|O.......|.....O..|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 117.89,\n",
-       "  OOOOOOOO|....O...|......O.|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 184.98,\n",
-       "  .....O..|OOOOOOOO|.....O..|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 170.69,\n",
-       "  ......O.|......O.|......O.|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 55.26,\n",
-       "  ......O.|OOOOOOOO|......O.|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 3 fitness: 302.75,\n",
-       "  ......O.|O.......|......O.|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 25.96,\n",
-       "  .......O|OOOOOOOO|.....O..|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 215.48,\n",
-       "  OOOOOOOO|.O......|......O.|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 93.29,\n",
-       "  ....O...|.O......|......O.|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 58.92,\n",
-       "  ..O.....|.....O..|OOOOOOOO|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 215.51,\n",
-       "  .O......|.......O|.O......|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 69.27,\n",
-       "  .O......|.......O|.O......|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 47.28,\n",
-       "  OOOOOOOO|.....O..|.......O|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 3 fitness: 219.66,\n",
-       "  ...O....|.......O|OOOOOOOO|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 231.90,\n",
-       "  OOOOOOOO|.......O|.......O|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 6 fitness: 239.17,\n",
-       "  OOOOOOOO|.....O..|....O...|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 3 fitness: 189.52,\n",
-       "  .....O..|OOOOOOOO|....O...|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 208.91,\n",
-       "  ...O....|.....O..|..O.....|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 39.64,\n",
-       "  ...O....|.....O..|..O.....|O.......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 6 fitness: 85.50,\n",
-       "  ...O....|.....O..|OOOOOOOO|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 183.75,\n",
-       "  ..O.....|OOOOOOOO|....O...|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 119.40,\n",
-       "  .....O..|.......O|.......O|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....O.. x 1 fitness: 180.42,\n",
-       "  .O......|.....O..|OOOOOOOO|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 5 fitness: 271.09,\n",
-       "  .O......|.....O..|OOOOOOOO|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 186.09,\n",
-       "  .O......|.....O..|O.......|OO......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 76.94,\n",
-       "  .O......|.....O..|O.......|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 37.83,\n",
-       "  ....O...|.O......|.....O..|OOOO....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 3 fitness: 92.74,\n",
-       "  ....O...|OOOOOOOO|.....O..|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 211.92,\n",
-       "  ....O...|.O......|.....O..|O.......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 60.10,\n",
-       "  ...O....|.......O|O.......|O.......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 5 fitness: 67.92,\n",
-       "  O.......|.......O|OOOOOOOO|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 17 fitness: 333.76,\n",
-       "  ....O...|OOOOOOOO|....O...|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 3 fitness: 212.26,\n",
-       "  ......O.|OOOOOOOO|.....O..|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 4 fitness: 312.31,\n",
-       "  ......O.|.....O..|.....O..|OO......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 100.45,\n",
-       "  OOOOOOOO|.....O..|.....O..|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 226.14,\n",
-       "  ......O.|OOOOOOOO|.....O..|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 215.53,\n",
-       "  .O......|OOOOOOOO|......O.|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 138.17,\n",
-       "  .O......|OOOOOOOO|......O.|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 87.19,\n",
-       "  ..O.....|OOOOOOOO|.......O|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 108.75,\n",
-       "  ..O.....|.......O|OOOOOOOO|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 212.50,\n",
-       "  ..OOOOOO|....O...|......O.|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 5 fitness: 224.44,\n",
-       "  .O......|O.......|OOOOOOOO|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 19 fitness: 11.84,\n",
-       "  ......O.|OOOOOOOO|....O...|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 3 fitness: 209.22,\n",
-       "  ...O....|.......O|OOOOOOOO|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 3 fitness: 188.18,\n",
-       "  OOOO....|..O.....|OOOOOOOO|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 20 fitness: 0.00,\n",
-       "  .......O|OOOOOOOO|....O...|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 244.91,\n",
-       "  ..O.....|.O......|OOOOOOOO|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 14 fitness: 8.40,\n",
-       "  ..O.....|.......O|.......O|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 52.70,\n",
-       "  OOOOOOOO|......O.|.....O..|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 3 fitness: 210.60,\n",
-       "  O.......|......O.|OOOOOOOO|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 6 fitness: 328.13,\n",
-       "  OOOOOOOO|.O......|....O...|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 90.26,\n",
-       "  OOOOOOOO|....O...|.......O|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 185.82,\n",
-       "  .O......|.....O..|..O.....|O.......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 4 fitness: 77.92,\n",
-       "  ......O.|OOOOOOOO|.......O|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 203.56,\n",
-       "  ..OOOOOO|....O...|.......O|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 7 fitness: 201.66,\n",
-       "  OOOOOOOO|.......O|......O.|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 3 fitness: 221.39,\n",
-       "  .......O|....O...|.O......|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 37.75,\n",
-       "  ....O...|OOOOOOOO|.......O|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 172.49,\n",
-       "  ......O.|OOOO....|....O...|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 14 fitness: 201.72,\n",
-       "  OOOOOOOO|....O...|....O...|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 223.88,\n",
-       "  O.......|....O...|....O...|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 54.79,\n",
-       "  .O......|.......O|.......O|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 37.39,\n",
-       "  OOOOOOOO|.......O|.......O|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 179.14,\n",
-       "  OOOOOOOO|......O.|....O...|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 283.20,\n",
-       "  ....O...|......O.|.....O..|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 81.96,\n",
-       "  ....O...|O.......|.....O..|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 53.06,\n",
-       "  .O......|.......O|......O.|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 68.35,\n",
-       "  OOOOOOOO|.O......|.O......|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 1 fitness: 3.75,\n",
-       "  .......O|...O....|.....O..|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 62.66,\n",
-       "  ......OO|OOOO....|.....O..|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 15 fitness: 240.30,\n",
-       "  O.......|.....O..|O.......|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 44.85,\n",
-       "  ..O.....|.......O|....O...|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 59.76,\n",
-       "  OOOOOOOO|.......O|....O...|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 5 fitness: 195.19,\n",
-       "  O.......|......O.|....O...|.OO.....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 84.36,\n",
-       "  O.......|......O.|....O...|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 3 fitness: 65.53,\n",
-       "  O.......|......O.|....O...|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 63.13,\n",
-       "  .......O|.O......|.....O..|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 81.48,\n",
-       "  OOOOOOOO|.....O..|......O.|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 3 fitness: 255.42,\n",
-       "  ..O.....|.....O..|......O.|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 51.21,\n",
-       "  ..O.....|....O...|....O...|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 52.49,\n",
-       "  ..O.....|....O...|....O...|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 53.11,\n",
-       "  ......O.|.......O|......O.|OO......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 84.95,\n",
-       "  ......O.|.......O|OOOOOOOO|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 151.19,\n",
-       "  ..O.....|.....O..|.......O|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 3 fitness: 56.51,\n",
-       "  ......O.|..O.....|...O....|OOOOOOOO\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|OOOOOOOO x 2 fitness: 45.55,\n",
-       "  ..OOOOOO|....O...|O.......|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 76.52,\n",
-       "  ......O.|....O...|......O.|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 59.76,\n",
-       "  ......OO|OOOO....|.......O|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 4 fitness: 229.47,\n",
-       "  .......O|......O.|.....O..|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 2 fitness: 63.12,\n",
-       "  ......O.|OOOO....|......O.|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 8 fitness: 201.96,\n",
-       "  ......OO|OOOO....|......O.|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 9 fitness: 233.47,\n",
-       "  ...O....|....O...|O.......|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 3 fitness: 62.33,\n",
-       "  ....O...|.....O..|.....O..|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 66.65,\n",
-       "  OOOOOOOO|....O...|.....O..|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 195.11,\n",
-       "  ..O.....|......O.|......O.|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 65.11,\n",
-       "  O.......|......O.|.O......|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 3 fitness: 76.88,\n",
-       "  O.......|......O.|.O......|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 59.44,\n",
-       "  ......O.|.......O|....O...|OO......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 5 fitness: 94.85,\n",
-       "  ....O...|..O.....|......O.|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 58.84,\n",
-       "  ....O...|..O.....|......O.|O.......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 2 fitness: 82.47,\n",
-       "  ....O...|..O.....|......O.|OO......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 3 fitness: 111.75,\n",
-       "  ....O...|.O......|......O.|OOOO....\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 3 fitness: 147.77,\n",
-       "  ....O...|.O......|......O.|O.......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 3 fitness: 80.39,\n",
-       "  ......O.|..O.....|.....O..|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 42.57,\n",
-       "  ..O.....|......O.|O.......|O.......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.....OOO x 7 fitness: 75.84,\n",
-       "  ..O.....|......O.|O.......|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 55.17,\n",
-       "  .O......|......O.|....O...|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 3 fitness: 60.37,\n",
-       "  .O......|......O.|OOOOOOOO|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 209.02,\n",
-       "  .O......|......O.|....O...|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 85.00,\n",
-       "  .O......|......O.|....O...|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 2 fitness: 64.80,\n",
-       "  ..O.....|......O.|....O...|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|......OO x 1 fitness: 55.70,\n",
-       "  ..O.....|......O.|....O...|.O......\t1\tOOOOOOOO|OOOOOOOO|OOOOOOOO|.......O x 1 fitness: 53.58,\n",
-       "  ...],\n",
-       " [{'trial': 0,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 1,\n",
-       "   'numerosity': 1,\n",
-       "   'reliable': 0},\n",
-       "  {'trial': 5,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 3,\n",
-       "   'numerosity': 3,\n",
-       "   'reliable': 0},\n",
-       "  {'trial': 10,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 5,\n",
-       "   'numerosity': 5,\n",
-       "   'reliable': 0},\n",
-       "  {'trial': 15,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 7,\n",
-       "   'numerosity': 7,\n",
-       "   'reliable': 0},\n",
-       "  {'trial': 20,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 8,\n",
-       "   'numerosity': 8,\n",
-       "   'reliable': 0},\n",
-       "  {'trial': 25,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 11,\n",
-       "   'numerosity': 11,\n",
-       "   'reliable': 0},\n",
-       "  {'trial': 30,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 13,\n",
-       "   'numerosity': 13,\n",
-       "   'reliable': 0},\n",
-       "  {'trial': 35,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 15,\n",
-       "   'numerosity': 15,\n",
-       "   'reliable': 0},\n",
-       "  {'trial': 40,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 16,\n",
-       "   'numerosity': 16,\n",
-       "   'reliable': 0},\n",
-       "  {'trial': 45,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 18,\n",
-       "   'numerosity': 18,\n",
-       "   'reliable': 0},\n",
-       "  {'trial': 50,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 22,\n",
-       "   'numerosity': 22,\n",
-       "   'reliable': 0},\n",
-       "  {'trial': 55,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 24,\n",
-       "   'numerosity': 24,\n",
-       "   'reliable': 0},\n",
-       "  {'trial': 60,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 26,\n",
-       "   'numerosity': 26,\n",
-       "   'reliable': 0},\n",
-       "  {'trial': 65,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 29,\n",
-       "   'numerosity': 29,\n",
-       "   'reliable': 0},\n",
-       "  {'trial': 70,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 33,\n",
-       "   'numerosity': 33,\n",
-       "   'reliable': 0},\n",
-       "  {'trial': 75,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 36,\n",
-       "   'numerosity': 36,\n",
-       "   'reliable': 0},\n",
-       "  {'trial': 80,\n",
-       "   'steps_in_trial': 1,\n",
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-       "   'reliable': 32},\n",
-       "  ...])"
+       "[[0.28;1.00][0.76;0.76][0.00;1.00][0.00;1.00]\t0\t[0.00;1.00][0.00;1.00][0.00;1.00][0.00;1.00] x 1 fitness: 217.18,\n",
+       " [0.28;1.00][0.48;0.48][0.00;1.00][0.00;1.00]\t0\t[0.00;1.00][0.00;1.00][0.00;1.00][0.00;1.00] x 1 fitness: 206.32,\n",
+       " [0.00;1.00][0.17;0.17][0.55;1.00][0.00;1.00]\t0\t[0.00;1.00][0.00;1.00][0.00;1.00][0.00;1.00] x 1 fitness: 184.56,\n",
+       " [0.00;1.00][0.00;1.00][0.00;1.00][0.00;0.03]\t1\t[0.00;1.00][0.00;1.00][0.00;1.00][0.97;1.00] x 1 fitness: 181.35,\n",
+       " [0.97;0.97][0.00;1.00][0.55;1.00][0.00;1.00]\t0\t[0.00;1.00][0.00;1.00][0.00;1.00][0.00;1.00] x 1 fitness: 175.33,\n",
+       " [0.29;1.00][0.85;0.85][0.00;1.00][0.00;1.00]\t1\t[0.00;1.00][0.00;1.00][0.00;1.00][0.00;1.00] x 1 fitness: 172.66,\n",
+       " [0.00;1.00][0.98;1.00][0.35;0.35][0.00;1.00]\t0\t[0.00;1.00][0.00;1.00][0.00;1.00][0.00;1.00] x 2 fitness: 167.55,\n",
+       " [0.00;1.00][0.44;0.44][0.55;1.00][0.00;1.00]\t0\t[0.00;1.00][0.00;1.00][0.00;1.00][0.00;1.00] x 1 fitness: 166.57,\n",
+       " [0.42;0.42][0.98;1.00][0.00;1.00][0.00;1.00]\t0\t[0.00;1.00][0.00;1.00][0.00;1.00][0.00;1.00] x 2 fitness: 157.94,\n",
+       " [0.22;0.22][0.00;1.00][0.55;1.00][0.00;1.00]\t0\t[0.00;1.00][0.00;1.00][0.00;1.00][0.00;1.00] x 1 fitness: 154.99]"
       ]
      },
      "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "evaluate(rmpx, encoder_bits=3, trials=20_000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 1, 'reward': 0, 'population': 1, 'numerosity': 1, 'reliable': 0}\n",
-      "INFO:lcs.agents.Agent:{'trial': 5000, 'steps_in_trial': 1, 'reward': 0, 'population': 3567, 'numerosity': 4761, 'reliable': 4}\n",
-      "INFO:lcs.agents.Agent:{'trial': 10000, 'steps_in_trial': 1, 'reward': 0, 'population': 4611, 'numerosity': 7101, 'reliable': 23}\n",
-      "INFO:lcs.agents.Agent:{'trial': 15000, 'steps_in_trial': 1, 'reward': 1000, 'population': 5443, 'numerosity': 8996, 'reliable': 41}\n",
-      "INFO:lcs.agents.Agent:{'trial': 20000, 'steps_in_trial': 1, 'reward': 1000, 'population': 6079, 'numerosity': 10715, 'reliable': 49}\n",
-      "INFO:lcs.agents.Agent:{'trial': 25000, 'steps_in_trial': 1, 'reward': 1000, 'population': 6602, 'numerosity': 12052, 'reliable': 71}\n",
-      "INFO:lcs.agents.Agent:{'trial': 30000, 'steps_in_trial': 1, 'reward': 1000, 'population': 7060, 'numerosity': 13335, 'reliable': 92}\n",
-      "INFO:lcs.agents.Agent:{'trial': 35000, 'steps_in_trial': 1, 'reward': 0, 'population': 7437, 'numerosity': 14536, 'reliable': 122}\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "([OOOOOOOOOO|OOOOOOOOOO|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 123.95,\n",
-       "  OOOOOOOOOO|OOOOOOOOOO|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 168.76,\n",
-       "  ......O...|OOOOOOOOOO|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 2 fitness: 193.44,\n",
-       "  OOOOOOOOOO|..O.......|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 161.70,\n",
-       "  OOOOOOOOOO|.O........|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 171.84,\n",
-       "  .....O....|OOOOOOOOOO|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 106.46,\n",
-       "  OOOOOOOOOO|OOOOOOOOOO|......O...|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 105.17,\n",
-       "  OOOOOOOOOO|.........O|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 97.48,\n",
-       "  .O........|....O.....|.....O....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 21.22,\n",
-       "  OOOOOOOOOO|OOOOOOOOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 152.18,\n",
-       "  .....O....|.O........|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 68.36,\n",
-       "  OOOOOOOOOO|OOOOOOOOOO|..O.......|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 247.78,\n",
-       "  ........O.|.........O|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 77.71,\n",
-       "  OOOOOOOOOO|..O.......|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 153.36,\n",
-       "  OOOOOOOOOO|OOOOOOOOOO|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 205.83,\n",
-       "  ..O.......|.O........|........O.|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 23.22,\n",
-       "  ....O.....|....O.....|.....O....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 17.69,\n",
-       "  OOOOOOOOOO|.O........|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 82.83,\n",
-       "  .......O..|.O........|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 78.69,\n",
-       "  ....O.....|...O......|......O...|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 20.82,\n",
-       "  ....O.....|OOOOOOOOOO|......O...|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 59.10,\n",
-       "  ........O.|O.........|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 22.13,\n",
-       "  OOOOOOOOOO|..O.......|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 87.10,\n",
-       "  OOOOOOOOOO|.....O....|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 92.92,\n",
-       "  ........O.|.....O....|.O........|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 17.93,\n",
-       "  .....O....|.....O....|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 80.28,\n",
-       "  .....O....|OOOOOOOOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 89.73,\n",
-       "  OOOOOOOOOO|.O........|.....O....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 94.12,\n",
-       "  OOOOOOOOOO|..O.......|...O......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 88.53,\n",
-       "  .........O|OOOOOOOOOO|.....O....|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 16 fitness: 7.07,\n",
-       "  ........O.|OOOOOOOOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 90.58,\n",
-       "  ........O.|......O...|.O........|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 2 fitness: 17.64,\n",
-       "  OOOOOOOOOO|.........O|..O.......|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 138.35,\n",
-       "  ......O...|OOOOOOOOOO|..O.......|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 2 fitness: 182.25,\n",
-       "  ..O.......|..O.......|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 93.72,\n",
-       "  ....O.....|.O........|.........O|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 17.87,\n",
-       "  .O........|.O........|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 2 fitness: 103.65,\n",
-       "  .O........|OOOOOOOOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 99.79,\n",
-       "  .O........|.O........|.O........|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 2 fitness: 20.60,\n",
-       "  ......O...|OOOOOOOOOO|.....O....|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 15 fitness: 19.90,\n",
-       "  OOOOOOOOOO|....O.....|O.........|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 106.04,\n",
-       "  .....O....|....O.....|OOOOOOOOOO|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 80.28,\n",
-       "  ........O.|OOOOOOOOOO|.....O....|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 14 fitness: 13.16,\n",
-       "  ....O.....|.O........|.......O..|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 15.04,\n",
-       "  .........O|..O.......|.O........|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 18.30,\n",
-       "  ....O.....|..O.......|...O......|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 17.43,\n",
-       "  .O........|..O.......|.O........|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 2 fitness: 16.95,\n",
-       "  .O........|..O.......|OOOOOOOOOO|..OOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 10 fitness: 230.17,\n",
-       "  .O........|....O.....|.....O....|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 19.40,\n",
-       "  ....O.....|....O.....|OOOOOOOOOO|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 88.06,\n",
-       "  .O........|.....O....|OOOOOOOOOO|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 18 fitness: 17.57,\n",
-       "  OOOOOOOOOO|..O.......|..O.......|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 2 fitness: 156.77,\n",
-       "  ..O.......|..O.......|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 6 fitness: 148.83,\n",
-       "  .O........|.....O....|OOOOOOOOOO|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 14 fitness: 11.26,\n",
-       "  ....O.....|.O........|OOOOOOOOOO|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 101.25,\n",
-       "  OOOOO.....|O.........|OOOOO.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 5 fitness: 234.52,\n",
-       "  .........O|OOOOOOOOOO|........O.|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 18 fitness: 22.59,\n",
-       "  ......O...|OOOOOOOOOO|....O.....|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 120.49,\n",
-       "  OOOOOOOOOO|..O.......|..O.......|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 152.39,\n",
-       "  OOOOO.....|.O........|OOOOO.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 197.84,\n",
-       "  .O........|....O.....|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.....OOOOO x 2 fitness: 216.24,\n",
-       "  ......O...|OOOOOOOOOO|.O........|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 108.74,\n",
-       "  .......O..|OOOOOOOOOO|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 93.34,\n",
-       "  OOOOOOOOOO|..O.......|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 90.61,\n",
-       "  .......O..|OOOOOOOOOO|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 61.46,\n",
-       "  .......O..|OOOOOOOOOO|........O.|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 18 fitness: 31.20,\n",
-       "  ........O.|OOOOOOOOOO|.O........|..OOOO....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.....OOOOO x 7 fitness: 158.64,\n",
-       "  ...O......|..O.......|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 108.09,\n",
-       "  OOOOOOOOOO|..O.......|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 259.75,\n",
-       "  .........O|OOOOOOOOOO|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 78.65,\n",
-       "  OOOOOOOOOO|O.........|O.........|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 131.47,\n",
-       "  .O........|O.........|OOOOOOOOOO|..OOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 3 fitness: 218.98,\n",
-       "  OOOOOOOOOO|.....O....|.....O....|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 2 fitness: 18.18,\n",
-       "  OOOOO.....|.O........|OOOOO.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 6 fitness: 220.42,\n",
-       "  ......O...|..O.......|OOOOOOOOOO|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 79.37,\n",
-       "  OOOOOOOOOO|OOOO......|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 196.53,\n",
-       "  ....O.....|....O.....|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.....OOOOO x 1 fitness: 186.36,\n",
-       "  ..O.......|O.........|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 121.09,\n",
-       "  OOOOOOOOOO|OOOO......|O.........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 189.93,\n",
-       "  .O........|..O.......|OOOOOOOOOO|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 114.67,\n",
-       "  OOOOOOOOOO|OOOO......|.........O|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 125.81,\n",
-       "  OOOOOOOOOO|OOOO......|...O......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 247.89,\n",
-       "  OOOOOOOOOO|OOOO......|.......O..|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 142.10,\n",
-       "  .........O|OOOOOOOOOO|......O...|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 14 fitness: 8.52,\n",
-       "  ..O.......|.O........|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 101.91,\n",
-       "  OOOOOOOOOO|OOOO......|.O........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 202.39,\n",
-       "  ...O......|........O.|OOOOOOOOOO|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 4 fitness: 35.87,\n",
-       "  OOOOOOOOOO|..O.......|...O......|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 82.29,\n",
-       "  OOOOOOOOOO|..O.......|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 206.03,\n",
-       "  OOOOOOOOOO|OOOO......|........O.|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 144.24,\n",
-       "  OOOOOOOOOO|OOOO......|......O...|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 174.41,\n",
-       "  OOOOOOOOOO|OOOO......|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 216.51,\n",
-       "  OOOOOOOOOO|....O.....|.O........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.....OOOOO x 2 fitness: 222.29,\n",
-       "  ........O.|OOOOOOOOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 167.23,\n",
-       "  OOOOOOOOOO|OOOOOOOOOO|O.........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 258.77,\n",
-       "  .O........|..O.......|OOOOOOOOOO|OO........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 114.30,\n",
-       "  .O........|OOOOOOOOOO|..O.......|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 138.54,\n",
-       "  .O........|..O.......|OOOOOOOOOO|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 113.19,\n",
-       "  OOOOOOOOOO|OOOO......|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 197.34,\n",
-       "  .........O|...OOOOOOO|.O........|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 4 fitness: 162.91,\n",
-       "  OOOOOOOOOO|....O.....|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 207.41,\n",
-       "  ..O.......|.....O....|OOOOOOOOOO|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 20 fitness: 14.96,\n",
-       "  .O........|..O.......|OOOOOOOOOO|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 74.36,\n",
-       "  OOOOOOOOOO|OOOO......|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 181.90,\n",
-       "  OOOOOOOOOO|OOOOOOOOOO|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 218.03,\n",
-       "  ....O.....|..O.......|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 133.99,\n",
-       "  OOOOOOOOOO|OOOO......|........O.|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 114.34,\n",
-       "  ....O.....|..O.......|........O.|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 19.31,\n",
-       "  .O........|...O......|OOOOOOOOOO|..OOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 6 fitness: 233.50,\n",
-       "  OOOOOOOOOO|...O......|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 259.84,\n",
-       "  OOOOOOOOOO|...O......|..O.......|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 144.89,\n",
-       "  ........O.|....O.....|........O.|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 86.60,\n",
-       "  .........O|...OOOOOOO|...O......|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 10 fitness: 180.62,\n",
-       "  ........O.|OOOOOOOOOO|...O......|..OOOO....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.....OOOOO x 4 fitness: 187.45,\n",
-       "  ........O.|.....O....|...O......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 21.81,\n",
-       "  OOOOOOOOOO|OOOOOOOOOO|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 149.55,\n",
-       "  OOOOOOOOOO|O.........|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 4 fitness: 250.53,\n",
-       "  .....O....|OOOOOOOOOO|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 98.85,\n",
-       "  .........O|OOOOOOOOOO|.....O....|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 16 fitness: 8.42,\n",
-       "  .........O|..O.......|OOOOOOOOOO|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 131.16,\n",
-       "  ........O.|OOOOOOOOOO|.....O....|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 19 fitness: 25.11,\n",
-       "  ......O...|OOOOOOOOOO|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 106.77,\n",
-       "  ......O...|....O.....|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 24.14,\n",
-       "  OOOOOOOOOO|....O.....|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 128.41,\n",
-       "  ........O.|OOOOOOOOOO|....O.....|..OOOO....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.....OOOOO x 2 fitness: 93.08,\n",
-       "  ........O.|.O........|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 24.14,\n",
-       "  OOOOOOOOOO|.....O....|..O.......|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 161.95,\n",
-       "  ........O.|.....O....|..O.......|OOO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 57.24,\n",
-       "  ......O...|........O.|...O......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 24.41,\n",
-       "  ........O.|........O.|...O......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 25.84,\n",
-       "  .......O..|.......O..|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 25.96,\n",
-       "  .......O..|.......O..|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 120.02,\n",
-       "  OOOOOOOOOO|.......O..|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 95.31,\n",
-       "  .O........|.O........|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 27.24,\n",
-       "  .O........|..O.......|.......O..|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 27.83,\n",
-       "  OOOOOOOOOO|.........O|......O...|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 2 fitness: 11.45,\n",
-       "  .O........|........O.|..O.......|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 55.46,\n",
-       "  ..O.......|..O.......|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 134.89,\n",
-       "  ...O......|....O.....|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 125.23,\n",
-       "  OOOOOOOOOO|..O.......|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 130.81,\n",
-       "  O.........|..O.......|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 169.81,\n",
-       "  ........O.|.O........|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 67.31,\n",
-       "  OOOOOOOOOO|OOOO......|......O...|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 161.23,\n",
-       "  OOOOOOOOOO|OOOOOOOOOO|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 156.79,\n",
-       "  OOOOOOOOOO|.......O..|.........O|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 30.22,\n",
-       "  .........O|..O.......|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 100.58,\n",
-       "  OOOOOOOOOO|OOOO......|.........O|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 141.47,\n",
-       "  OOOOOOOOOO|........O.|..O.......|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 164.02,\n",
-       "  ......O...|OOOOOOOOOO|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 203.66,\n",
-       "  .....O....|......O...|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 87.63,\n",
-       "  OOOOOOOOOO|..O.......|........O.|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 100.16,\n",
-       "  .O........|......OOOO|OOOOOOOOOO|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 13 fitness: 0.01,\n",
-       "  .........O|..O.......|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 109.09,\n",
-       "  .........O|..O.......|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 22.89,\n",
-       "  ..O.......|......O...|..O.......|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 68.20,\n",
-       "  .........O|.........O|........O.|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 72.45,\n",
-       "  .........O|OOOOOOOOOO|......O...|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 19 fitness: 13.92,\n",
-       "  .O........|....O.....|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 108.11,\n",
-       "  .O........|OOOOOO....|.......O..|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 73.88,\n",
-       "  .O........|....O.....|.......O..|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 21.97,\n",
-       "  .......O..|.......O..|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 22.82,\n",
-       "  OOOOOOOOOO|....O.....|..O.......|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 151.02,\n",
-       "  ...O......|OOOOOOOOOO|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 127.89,\n",
-       "  .......O..|OOOOOOOOOO|.....O....|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 12 fitness: 12.43,\n",
-       "  OOOOOOOOOO|.........O|.........O|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 13.23,\n",
-       "  OOOOOOOO..|.O........|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 178.21,\n",
-       "  .O........|.......O..|O.........|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 78.21,\n",
-       "  ..O.......|....O.....|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.....OOOOO x 1 fitness: 208.22,\n",
-       "  ..O.......|....O.....|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 141.85,\n",
-       "  ..O.......|....O.....|........O.|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 3 fitness: 31.36,\n",
-       "  .........O|OOOOOOOOOO|..O.......|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 3 fitness: 159.20,\n",
-       "  ....O.....|......O...|.......O..|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 93.65,\n",
-       "  ....O.....|....O.....|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 109.74,\n",
-       "  OOOOOOOO..|OOOOOOOOOO|...O......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 163.44,\n",
-       "  ......O...|OOOOOOOOOO|..O.......|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 157.05,\n",
-       "  ......O...|...O......|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 122.91,\n",
-       "  OOOOOOOOOO|.O........|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 138.63,\n",
-       "  OOOOOOOO..|OOOOOOOOOO|......O...|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 56.10,\n",
-       "  OOOOOOOO..|O.........|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 130.26,\n",
-       "  .........O|OOOOOOOOOO|...O......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 84.53,\n",
-       "  OOOOOOOOOO|..O.......|...O......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 114.23,\n",
-       "  OOOOOOOOOO|OOOOOOOOOO|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 222.51,\n",
-       "  .........O|OOOOOOOOOO|.O........|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 3 fitness: 161.02,\n",
-       "  OOOOOOOOOO|OOOOOOOOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 230.15,\n",
-       "  OOOOOOOOOO|..O.......|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 132.06,\n",
-       "  ....O.....|..O.......|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 22.30,\n",
-       "  OOOOOOOOOO|..O.......|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 88.88,\n",
-       "  ....O.....|..O.......|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 118.93,\n",
-       "  ....O.....|OOOOOOOOOO|......O...|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 93.79,\n",
-       "  ....O.....|.O........|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 108.27,\n",
-       "  OOOOOOOOOO|.O........|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 105.41,\n",
-       "  OOOOOOOOOO|OOOOOOOOOO|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 122.82,\n",
-       "  OOOOOOOOOO|.......O..|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 131.77,\n",
-       "  .......O..|.......O..|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 29.05,\n",
-       "  .......O..|OOOOOOOOOO|....O.....|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 6 fitness: 200.92,\n",
-       "  .........O|OOOOOOOOOO|..O.......|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 151.02,\n",
-       "  .........O|OOOOOOOOOO|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 80.87,\n",
-       "  .........O|........O.|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 26.80,\n",
-       "  .........O|......OOOO|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 3 fitness: 95.27,\n",
-       "  OOOOOOOOOO|....O.....|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 131.54,\n",
-       "  ......O...|OOOOOOOOOO|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 2 fitness: 194.10,\n",
-       "  ......O...|....O.....|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 27.99,\n",
-       "  OOOOOOOOOO|....O.....|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 111.56,\n",
-       "  .....O....|OOOOOOOOOO|..O.......|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 172.52,\n",
-       "  .....O....|OOOOOOOOOO|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 125.08,\n",
-       "  ....O.....|OOOOOOOOOO|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 102.95,\n",
-       "  ....O.....|OOOOOOOOOO|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 86.48,\n",
-       "  OOOOOOOOOO|..O.......|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 168.14,\n",
-       "  ........O.|OOOOOOOOOO|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 114.76,\n",
-       "  ..O.......|....O.....|.........O|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 22.74,\n",
-       "  ..O.......|....O.....|.........O|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 26.19,\n",
-       "  ..O.......|....O.....|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 187.33,\n",
-       "  ..O.......|OOOOOOOOOO|.........O|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 130.12,\n",
-       "  ........O.|OOOOOOOOOO|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 113.05,\n",
-       "  .......OOO|....O.....|....O.....|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 3 fitness: 78.17,\n",
-       "  OOOOOOOOOO|....O.....|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 188.59,\n",
-       "  OOOOOOOOOO|....O.....|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 116.67,\n",
-       "  ..O.......|..O.......|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 3 fitness: 180.90,\n",
-       "  .O........|..O.......|OOOOOOOOOO|..OOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 6 fitness: 236.91,\n",
-       "  .O........|OOOOOO....|.......O..|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 82.49,\n",
-       "  ....O.....|OOOOOOOOOO|.....O....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 97.50,\n",
-       "  ......O...|OOOOOOOOOO|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 157.01,\n",
-       "  OOOOOOOOOO|..O.......|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 115.62,\n",
-       "  .O........|....O.....|OOOOOOOOOO|..OOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 225.96,\n",
-       "  OOOOOOOOOO|....O.....|...O......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 4 fitness: 237.16,\n",
-       "  OOOOOOOOOO|....O.....|...O......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 132.72,\n",
-       "  OOOOOOOO..|....O.....|...O......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 95.72,\n",
-       "  OOOOOOOOOO|..O.......|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 121.43,\n",
-       "  O.........|..O.......|OOOOOOOOOO|..OO......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........O. x 6 fitness: 134.22,\n",
-       "  O.........|..O.......|..O.......|.OOO......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 1 fitness: 45.46,\n",
-       "  OOOOOOOO..|..O.......|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 95.80,\n",
-       "  O.........|..O.......|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 145.56,\n",
-       "  .........O|OOOOOOOOOO|....O.....|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 166.00,\n",
-       "  .........O|OOOOOOOOOO|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 132.97,\n",
-       "  .........O|........O.|....O.....|.OOO......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 1 fitness: 34.03,\n",
-       "  O.........|...O......|OOOOOOOOOO|..OO......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........O. x 3 fitness: 146.68,\n",
-       "  O.........|...O......|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 110.02,\n",
-       "  ......O...|....O.....|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 115.17,\n",
-       "  OOOOOOOOOO|....O.....|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 108.47,\n",
-       "  ......O...|OOOOOOOOOO|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 127.32,\n",
-       "  OOOOOOOOOO|....O.....|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 137.02,\n",
-       "  ..O.......|OOOOOO....|.......O..|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 3 fitness: 92.52,\n",
-       "  ..O.......|..O.......|.......O..|.OOO......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 1 fitness: 37.32,\n",
-       "  .......O..|OOOOOOOOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 172.77,\n",
-       "  OOOOOOOOOO|..O.......|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 215.09,\n",
-       "  OOOOOOOOOO|..O.......|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 133.22,\n",
-       "  .........O|......O...|.........O|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 21.83,\n",
-       "  .O........|OOOOOOOOOO|..O.......|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 145.81,\n",
-       "  .O........|O.........|..O.......|.OOO......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 1 fitness: 44.42,\n",
-       "  OOOOOOOO..|O.........|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 95.80,\n",
-       "  OOOOOOOOOO|O.........|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 127.66,\n",
-       "  OOOOOOOOOO|..O.......|......O...|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 138.97,\n",
-       "  ....O.....|..O.......|......O...|.OOO......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 1 fitness: 46.55,\n",
-       "  ....O.....|..O.......|......O...|.OOO......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 1 fitness: 48.50,\n",
-       "  ..O.......|....O.....|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 132.54,\n",
-       "  ..O.......|....O.....|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 137.72,\n",
-       "  .....O....|.........O|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 99.66,\n",
-       "  .....O....|......OOOO|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 4 fitness: 54.74,\n",
-       "  .....O....|.........O|..O.......|.OOO......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 1 fitness: 37.86,\n",
-       "  .........O|OOOOOOOOOO|........O.|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 13 fitness: 14.37,\n",
-       "  O.........|.........O|..O.......|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 86.20,\n",
-       "  .........O|O.........|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 73.90,\n",
-       "  .........O|OOOOOOO...|.O........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 13 fitness: 124.31,\n",
-       "  .........O|O.........|.O........|.OOO......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 1 fitness: 42.17,\n",
-       "  OOOOOOOOOO|O.........|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 122.78,\n",
-       "  ......O...|.O........|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 114.52,\n",
-       "  ......O...|OOOOOOOOOO|...O......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 155.27,\n",
-       "  OOOOOOOO..|.O........|...O......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 84.13,\n",
-       "  .........O|OOOOOOOOOO|........O.|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 19 fitness: 17.67,\n",
-       "  .O........|....O.....|OOOOOOOOOO|..OOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 198.86,\n",
-       "  OOOOOOOOOO|.......O..|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 114.44,\n",
-       "  .......O..|OOOOOOOOOO|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 3 fitness: 97.82,\n",
-       "  .........O|OOOOOOOOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 3 fitness: 194.56,\n",
-       "  .........O|......OOOO|.O........|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOOO x 6 fitness: 72.82,\n",
-       "  OOOOOOOOOO|....O.....|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 194.75,\n",
-       "  .O........|.O........|......O...|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 23.44,\n",
-       "  .O........|OOOOOOOOOO|......O...|OOO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 58.39,\n",
-       "  OOOOOOOO..|.O........|......O...|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 103.14,\n",
-       "  .O........|........O.|.O........|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 60.98,\n",
-       "  ....O.....|....O.....|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.....OOOOO x 1 fitness: 152.97,\n",
-       "  .O........|....O.....|......O...|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 15.01,\n",
-       "  OOOOOOOO..|....O.....|......O...|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 89.30,\n",
-       "  ..O.......|.........O|O.........|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 9.64,\n",
-       "  ...O......|.....O....|OOOOOOO...|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 14 fitness: 36.56,\n",
-       "  .........O|OOOOOOOOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 116.87,\n",
-       "  .........O|..OOOOOOOO|.O........|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 138.50,\n",
-       "  .........O|......OOOO|.O........|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 5 fitness: 107.90,\n",
-       "  .....O....|OOOOOOOOOO|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 122.36,\n",
-       "  .....O....|.O........|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 97.76,\n",
-       "  .....O....|OOOOOOOOOO|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 111.44,\n",
-       "  .........O|.........O|........O.|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 23.32,\n",
-       "  .....O....|OOOOOOOOOO|.....O....|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 13 fitness: 17.30,\n",
-       "  ........O.|OOOOOOOOOO|..O.......|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 138.43,\n",
-       "  ........O.|OOOOOOOOOO|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 93.98,\n",
-       "  ........O.|..OOOOOOOO|..O.......|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 128.88,\n",
-       "  ........O.|.....O....|OOOOOOO...|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 3 fitness: 143.82,\n",
-       "  .........O|........O.|.........O|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 80.76,\n",
-       "  ........O.|OOOOOOOOOO|O.........|..OOOO....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.....OOOOO x 4 fitness: 158.45,\n",
-       "  OOOOOOOOOO|...O......|O.........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 135.46,\n",
-       "  ........O.|OOOOOOOOOO|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 109.94,\n",
-       "  ........O.|..OOOOOOOO|O.........|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 3 fitness: 202.59,\n",
-       "  .......O..|..O.......|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 126.37,\n",
-       "  OOOOOOOOOO|.O........|..O.......|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 3 fitness: 171.08,\n",
-       "  OOOOOOOO..|.O........|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 117.21,\n",
-       "  ......O...|......OOOO|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 3 fitness: 71.80,\n",
-       "  OOOOOOOO..|.........O|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 86.56,\n",
-       "  ....O.....|..O.......|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 179.67,\n",
-       "  OOOOOOOO..|..O.......|......O...|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 104.71,\n",
-       "  .O........|.O........|...O......|OO........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 23.20,\n",
-       "  OOOOOOOO..|.O........|...O......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 129.20,\n",
-       "  OOOOOOOOOO|.O........|...O......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 160.32,\n",
-       "  OOOOOOOOOO|.O........|...O......|.OOO......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 1 fitness: 247.71,\n",
-       "  ...O......|.....O....|OOOOOOOO..|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 29.95,\n",
-       "  ..O.......|OOOOOOOOOO|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 108.73,\n",
-       "  ..O.......|..OOOOOOOO|....O.....|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 4 fitness: 118.22,\n",
-       "  OOOOOOOOOO|..O.......|O.........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 143.16,\n",
-       "  OOOOOOOOOO|..O.......|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 112.06,\n",
-       "  .....O....|OOOOOOOOOO|O.........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 132.77,\n",
-       "  .....O....|OOOOOOOOOO|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 137.40,\n",
-       "  OOOOOOOOOO|.......O..|...O......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 111.60,\n",
-       "  ........O.|......OOOO|...O......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 2 fitness: 83.67,\n",
-       "  ........O.|..OOOOOOOO|...O......|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 202.59,\n",
-       "  .........O|OOOOOOOOOO|.......O..|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 20 fitness: 32.82,\n",
-       "  .O........|....O.....|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.....OOOOO x 1 fitness: 171.58,\n",
-       "  .O........|....O.....|.........O|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 21.17,\n",
-       "  .........O|....O.....|.........O|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 3 fitness: 103.38,\n",
-       "  ......O...|..O.......|.........O|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 2 fitness: 36.63,\n",
-       "  OOOOOOOOOO|....O.....|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 146.92,\n",
-       "  OOOOOOOOOO|....O.....|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 129.55,\n",
-       "  ......O...|..OOOOOOOO|....O.....|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 175.64,\n",
-       "  .........O|...O......|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 151.02,\n",
-       "  .........O|OOOOOOOOOO|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 109.71,\n",
-       "  .........O|OOOOOOOOOO|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 116.55,\n",
-       "  .........O|..OOOOOOOO|....O.....|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 2 fitness: 144.70,\n",
-       "  ....O.....|.O........|.....O....|OOO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 3 fitness: 36.24,\n",
-       "  ....O.....|.O........|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 113.87,\n",
-       "  ...O......|..O.......|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 205.05,\n",
-       "  ...O......|..O.......|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 116.37,\n",
-       "  OOOOOOOO..|..O.......|........O.|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 81.93,\n",
-       "  ...O......|..OOOOOOOO|........O.|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 2 fitness: 93.12,\n",
-       "  .........O|OOOOOOOOOO|...O......|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 4 fitness: 151.45,\n",
-       "  .......OOO|....O.....|...O......|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 82.15,\n",
-       "  ........O.|OOOOOOOOOO|...O......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 108.01,\n",
-       "  OOOOOOOOOO|....O.....|...O......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 125.42,\n",
-       "  ........O.|OOOOOOOOOO|...O......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 118.66,\n",
-       "  ........O.|OOOOOOOOOO|.........O|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 58.69,\n",
-       "  O.........|....O.....|OOOOOOOOOO|..OO......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........O. x 2 fitness: 121.09,\n",
-       "  OOOOOOOOOO|....O.....|.....O....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 116.03,\n",
-       "  O.........|OOOOOOOOOO|.....O....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 87.66,\n",
-       "  ........O.|OOOOOOOOOO|....O.....|..OOOO....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.....OOOOO x 1 fitness: 109.11,\n",
-       "  OOOOOOOOOO|......O...|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 129.78,\n",
-       "  ........O.|OOOOOOOOOO|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 149.31,\n",
-       "  .........O|.....O....|O.........|OOO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 32.10,\n",
-       "  .........O|OOOOOOOOOO|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 131.98,\n",
-       "  .........O|.....O....|O.........|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 18.22,\n",
-       "  .......O..|OOOOOOOOOO|..O.......|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 147.76,\n",
-       "  .......O..|OOOOOOOOOO|..O.......|.OOOOOO...\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.....OOOOO x 2 fitness: 274.40,\n",
-       "  OOOOOOOO..|......O...|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 90.05,\n",
-       "  .......O..|......OOOO|..O.......|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 4 fitness: 119.12,\n",
-       "  OOOOOOOOOO|......O...|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 78.24,\n",
-       "  ..O.......|..O.......|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 193.99,\n",
-       "  ..O.......|..O.......|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 128.52,\n",
-       "  ..O.......|..OOOOOOOO|........O.|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 3 fitness: 95.48,\n",
-       "  OOOOOOOOOO|......O...|...O......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 111.21,\n",
-       "  .........O|......OOOO|...O......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 2 fitness: 83.88,\n",
-       "  .........O|......O...|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 81.76,\n",
-       "  ......O...|OOOOOOOOOO|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 176.41,\n",
-       "  ......O...|OOOOOOOOOO|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 106.63,\n",
-       "  ......O...|..OOOOOOOO|....O.....|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 2 fitness: 155.67,\n",
-       "  ......O...|.....O....|OOOOOOOO..|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 95.22,\n",
-       "  ...OOOOOOO|.....O....|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 9 fitness: 169.28,\n",
-       "  ......O...|OOOOOOOOOO|O.........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 140.56,\n",
-       "  ......O...|OOOOOOOOOO|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 118.36,\n",
-       "  ...O......|..O.......|.....O....|OOO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 38.10,\n",
-       "  ...O......|..O.......|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 121.65,\n",
-       "  ...O......|..O.......|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 119.48,\n",
-       "  OOOOOOOOOO|....O.....|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 189.76,\n",
-       "  .......OOO|....O.....|.O........|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 75.18,\n",
-       "  ........O.|....O.....|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 139.22,\n",
-       "  ........O.|OOOOOOOOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 143.43,\n",
-       "  ........O.|..OOOOOOOO|.O........|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 2 fitness: 177.42,\n",
-       "  ......O...|OOOOOOOOOO|O.........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 142.27,\n",
-       "  ......O...|OOOOOOOOOO|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 139.31,\n",
-       "  ......O...|......OOOO|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 6 fitness: 91.48,\n",
-       "  ......O...|..OOOOOOOO|O.........|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 142.74,\n",
-       "  .........O|OOOOOOOOOO|O.........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 139.40,\n",
-       "  OOOOOOOOOO|....O.....|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 134.82,\n",
-       "  .......OOO|....O.....|O.........|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 3 fitness: 80.27,\n",
-       "  OOOOOOOOOO|....O.....|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 3 fitness: 221.17,\n",
-       "  .........O|OOOOOOO...|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 103.63,\n",
-       "  .........O|OOOOOOOOOO|O.........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 3 fitness: 117.37,\n",
-       "  OOOOOOOOOO|OOOOOOOOOO|O.........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 249.06,\n",
-       "  .........O|OOOOOOOOOO|.........O|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 15 fitness: 28.56,\n",
-       "  OOOOOOOOOO|....O.....|.O........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 123.03,\n",
-       "  .......O..|OOOOOOOOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 127.88,\n",
-       "  OOOOOOOOOO|.....O....|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 138.05,\n",
-       "  ......O...|OOOOOOOOOO|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 133.64,\n",
-       "  O.........|O.........|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 138.80,\n",
-       "  O.........|O.........|OOOOOOOOOO|..OO......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........O. x 3 fitness: 124.91,\n",
-       "  O.........|OOOOOOOOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 88.31,\n",
-       "  OOOOOOOO..|O.........|.O........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 100.48,\n",
-       "  OOOOOOOOOO|O.........|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 134.52,\n",
-       "  OOOOOOOOOO|O.........|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 206.68,\n",
-       "  OOOOOOOOOO|O.........|...O......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 143.16,\n",
-       "  .......O..|OOOOOOOOOO|...O......|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 8 fitness: 203.29,\n",
-       "  .......O..|OOOOOOOOOO|...O......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 124.72,\n",
-       "  OOOOOOOO..|O.........|...O......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 114.00,\n",
-       "  OOOOOOOOOO|O.........|...O......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 135.78,\n",
-       "  ...O......|OOOOOOOOOO|..O.......|.OOOOOO...\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.....OOOOO x 1 fitness: 127.55,\n",
-       "  ...O......|.O........|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 108.40,\n",
-       "  OOOOOOOOOO|.O........|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 172.78,\n",
-       "  ...O......|OOOOOOOOOO|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 98.08,\n",
-       "  OOOOOOOOOO|....O.....|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 155.53,\n",
-       "  ...O......|OOOOOOOOOO|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 180.40,\n",
-       "  OOOOOOOOOO|.O........|..O.......|.OOOOOO...\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.....OOOOO x 2 fitness: 247.67,\n",
-       "  OOOOOOOOOO|....O.....|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 4 fitness: 218.56,\n",
-       "  ....O.....|..OOOOOOOO|....O.....|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 3 fitness: 85.19,\n",
-       "  ....O.....|....O.....|OOOOOOOOOO|..OOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........O. x 9 fitness: 163.75,\n",
-       "  O.........|.........O|O.........|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 77.37,\n",
-       "  ........O.|OOOOOOOOOO|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 107.03,\n",
-       "  ........O.|..OOOOOOOO|....O.....|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 153.14,\n",
-       "  ........O.|..OOOO....|OOOOO.....|..OO......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 9 fitness: 247.95,\n",
-       "  ....O.....|O.........|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 147.10,\n",
-       "  OOOOOOOO..|O.........|.........O|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 104.20,\n",
-       "  ....O.....|O.........|.........O|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 24.02,\n",
-       "  ..O.......|OOOOOOOOOO|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 129.75,\n",
-       "  ..O.......|....O.....|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|....OOOO.. x 2 fitness: 187.76,\n",
-       "  ..O.......|OOOOOOOOOO|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOOO x 1 fitness: 62.53,\n",
-       "  ..O.......|..OOOOOOOO|O.........|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 2 fitness: 102.93,\n",
-       "  OOOOOOOOOO|....O.....|O.........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 132.57,\n",
-       "  .......O..|OOOOOOOOOO|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 107.03,\n",
-       "  .......O..|..OOOOOOOO|....O.....|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 3 fitness: 154.03,\n",
-       "  .......O..|......O...|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 87.74,\n",
-       "  ........O.|OOOOOOOOOO|......OOOO|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 17 fitness: 0.00,\n",
-       "  .........O|OOOOOOOOOO|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 113.94,\n",
-       "  .........O|........O.|....O.....|OOOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 1 fitness: 43.28,\n",
-       "  OOOOOOOOOO|........O.|....O.....|.OOO......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 1 fitness: 101.18,\n",
-       "  .........O|OOOOOOOOOO|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 97.29,\n",
-       "  OOOOOOOOOO|OOOOOOOOOO|....O.....|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 196.29,\n",
-       "  .........O|OOOOOOOOOO|..O.......|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 167.29,\n",
-       "  .........O|O.........|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 125.98,\n",
-       "  .........O|OO........|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 59.48,\n",
-       "  OOOOOOOOOO|O.........|OOOOOOOOOO|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 236.84,\n",
-       "  ..O.......|OOOOOOOOOO|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 84.25,\n",
-       "  ..O.......|OOOOOOOOOO|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 104.41,\n",
-       "  ..O.......|OOOOOOOOOO|OOOOOOOOOO|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 155.92,\n",
-       "  ........O.|OOOOOOOOOO|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 163.80,\n",
-       "  OOOOOOOOOO|O.........|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 127.51,\n",
-       "  ....O.....|..O.......|........O.|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 35.00,\n",
-       "  ....O.....|OOOOOOOOOO|OOOOOOOOOO|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 148.66,\n",
-       "  ......O...|OOOOOOOOOO|...O......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 203.73,\n",
-       "  ......O...|.O........|OOOOOOOOOO|.OOO......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 1 fitness: 76.41,\n",
-       "  OOOOOOOOOO|.O........|OOOOOOOOOO|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 220.92,\n",
-       "  ...O......|OOOOOOOOOO|......O...|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 106.02,\n",
-       "  ...O......|.O........|......O...|OO........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 24.91,\n",
-       "  ...O......|.O........|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 121.45,\n",
-       "  ...O......|OO........|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 3 fitness: 197.26,\n",
-       "  ...O......|OOOOOOOOOO|OOOOOOOOOO|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 160.13,\n",
-       "  OOOOOOOOOO|........O.|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 104.74,\n",
-       "  ........O.|......OOOO|....O.....|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 3 fitness: 98.40,\n",
-       "  .O........|....O.....|....OOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 7 fitness: 156.58,\n",
-       "  .O........|OOOOOOOOOO|OOOOOOOOOO|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 145.74,\n",
-       "  ........O.|.O........|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 69.53,\n",
-       "  OOOOOOOOOO|.O........|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 133.53,\n",
-       "  ........O.|.O........|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 86.39,\n",
-       "  OOOOOOOOOO|.O........|O.........|.OOO......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 10 fitness: 273.48,\n",
-       "  ........O.|OOOOOOOOOO|O.........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 143.27,\n",
-       "  .....O....|......O...|O.........|OOO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 36.93,\n",
-       "  OOOOOOOOOO|......O...|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 133.53,\n",
-       "  .....O....|......OOOO|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 55.60,\n",
-       "  OOOOOOOOOO|......O...|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 82.25,\n",
-       "  .....O....|OOOOOOOOOO|O.........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 146.02,\n",
-       "  .....O....|OOOOOOOOOO|OOOOOOOOOO|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 158.44,\n",
-       "  .....O....|OOOOOOOOOO|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 121.94,\n",
-       "  .....O....|..OOOOOOOO|....O.....|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 2 fitness: 157.39,\n",
-       "  .OOOOOOOOO|.......OOO|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 4 fitness: 73.10,\n",
-       "  ..O.......|....O.....|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 121.53,\n",
-       "  ..O.......|....O.....|.......O..|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 15.53,\n",
-       "  ..O.......|OOOOOO....|.......O..|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 3 fitness: 87.00,\n",
-       "  OOOOOOOOOO|OOOOOOOOOO|.......O..|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 174.14,\n",
-       "  OOOOOOOOOO|.O........|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 132.73,\n",
-       "  .........O|OOOOOOOOOO|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 158.19,\n",
-       "  .........O|OOOOOOOOOO|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 136.21,\n",
-       "  ...O......|.........O|OOOOOOOOOO|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 8 fitness: 12.85,\n",
-       "  OOOOOOOOOO|......O...|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 153.46,\n",
-       "  .O........|.O........|OOOOOOOOOO|..OOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 179.57,\n",
-       "  OOOOOOOO..|.O........|......O...|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 99.04,\n",
-       "  OOOOOOOOOO|.O........|......O...|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 92.69,\n",
-       "  ........O.|OOOOOOOOOO|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 121.94,\n",
-       "  ........O.|..OOOOOOOO|....O.....|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 2 fitness: 174.40,\n",
-       "  .........O|......OOOO|.........O|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 59.45,\n",
-       "  OOOOOOOOOO|O.........|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 136.77,\n",
-       "  .......O..|OOOOOOOOOO|O.........|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 9 fitness: 227.96,\n",
-       "  OOOOOOOO..|O.........|O.........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 102.38,\n",
-       "  .......O..|O.........|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 96.50,\n",
-       "  OOOOOOOOOO|O.........|O.........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 134.21,\n",
-       "  OOOOOOOOOO|OO........|O.........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 6 fitness: 188.37,\n",
-       "  OOOO......|OOO.......|........O.|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.....OOOO. x 16 fitness: 202.94,\n",
-       "  O.........|OOOOOOOOOO|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 145.79,\n",
-       "  O.........|OOOOOOOOOO|OOOOO.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 77.99,\n",
-       "  OOOOOOOOOO|..O.......|O.........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 214.16,\n",
-       "  OOOOOOOOOO|..O.......|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 165.43,\n",
-       "  ...O......|OOOOOOOOOO|O.........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 118.11,\n",
-       "  ...O......|OOOOOOOOOO|O.........|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 104.56,\n",
-       "  ..O.......|....O.....|OOOOOOOOOO|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......O... x 2 fitness: 84.39,\n",
-       "  ..O.......|....O.....|OOOOOOOOOO|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 96.51,\n",
-       "  OOOOOOOOOO|OO........|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 169.11,\n",
-       "  .....O....|OOOOOOOOOO|....O.....|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 117.90,\n",
-       "  ....O.....|OOOOOO....|.......O..|OO........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 9 fitness: 154.94,\n",
-       "  OOOOOOOOOO|..O.......|.......O..|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 113.94,\n",
-       "  .........O|........O.|OOOOOOOOOO|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 89.13,\n",
-       "  .........O|OOOOOOOOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 1 fitness: 157.54,\n",
-       "  .........O|........O.|OOOOOOOOOO|.OOO......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 1 fitness: 98.85,\n",
-       "  .........O|........O.|.O........|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 15.99,\n",
-       "  ....O.....|.........O|OOOOOOOOOO|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 31.02,\n",
-       "  OOOOOOOOOO|.O........|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 123.40,\n",
-       "  ......O...|.O........|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 106.30,\n",
-       "  .........O|..OOOOOOOO|....O.....|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 3 fitness: 165.95,\n",
-       "  .........O|OOOOOOOOOO|....O.....|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 107.34,\n",
-       "  ....O.....|OOOOOOOOOO|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 139.55,\n",
-       "  ....O.....|....O.....|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 169.51,\n",
-       "  ....O.....|..OOOOOOOO|O.........|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 5 fitness: 72.07,\n",
-       "  ......O...|..O.......|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 175.24,\n",
-       "  ......O...|OOOOOOOOOO|...O......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 121.33,\n",
-       "  OOOOOOOO..|..O.......|...O......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 95.06,\n",
-       "  ......O...|..OOOOOOOO|...O......|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 2 fitness: 102.12,\n",
-       "  ........O.|OOOOOOOOOO|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 142.76,\n",
-       "  OO........|......OOOO|OOOOOOOOOO|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 13 fitness: 0.00,\n",
-       "  ......O...|OOOOOOOOOO|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 123.90,\n",
-       "  .OOOOOOOOO|.......OOO|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 106.05,\n",
-       "  .....O....|OOOOOOOOOO|......O...|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 16 fitness: 37.29,\n",
-       "  .....O....|.O........|......O...|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 40.87,\n",
-       "  ........O.|.......O..|.O........|OOO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 50.35,\n",
-       "  ........O.|..OOOOOOOO|.O........|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 5 fitness: 172.95,\n",
-       "  .OOOOOOOOO|.......OOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 93.63,\n",
-       "  .OOOOOOOOO|.......OOO|...O......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 6 fitness: 142.66,\n",
-       "  OOOOOOOO..|O.........|........O.|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 92.40,\n",
-       "  OOOOOOOOOO|O.........|........O.|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 112.66,\n",
-       "  ....O.....|OO........|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 3 fitness: 183.30,\n",
-       "  ....O.....|OOOOOOOOOO|........O.|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 104.56,\n",
-       "  ....O.....|......O...|.........O|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 60.54,\n",
-       "  ....O.....|OOOOOOOOOO|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 104.37,\n",
-       "  ....O.....|..OOOOOOOO|..O.......|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 2 fitness: 111.73,\n",
-       "  ........O.|OOOOOOOOOO|.O........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 3 fitness: 124.72,\n",
-       "  ........O.|OOOOOOOOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 1 fitness: 185.55,\n",
-       "  OOOOOOOOOO|...O......|..O.......|.OOOOOO...\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.....OOOOO x 1 fitness: 215.32,\n",
-       "  .O........|..OOOOOOOO|..O.......|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 3 fitness: 116.87,\n",
-       "  .O........|OOOOOOOOOO|..O.......|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 90.75,\n",
-       "  .........O|OOOOOOOOOO|....O.....|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 2 fitness: 139.45,\n",
-       "  .........O|..O.......|....O.....|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 24.30,\n",
-       "  OOOOOOOOOO|..O.......|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 132.14,\n",
-       "  .........O|..OOOOOOOO|....O.....|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 150.74,\n",
-       "  ........O.|.O........|OOOOOOOOOO|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 132.89,\n",
-       "  .O........|O.........|.....O....|OOO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 26.47,\n",
-       "  OOOOOOOO..|O.........|.....O....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 99.67,\n",
-       "  .O........|O.........|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 121.89,\n",
-       "  OOOOOOOOOO|O.........|.....O....|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 126.09,\n",
-       "  .O........|O.........|.....O....|OO........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 27.59,\n",
-       "  .O........|O.........|OOOOOOOOOO|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 94.02,\n",
-       "  ....O.....|O.........|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 156.62,\n",
-       "  ....O.....|O.........|OOOOOOOOOO|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 128.58,\n",
-       "  ........O.|OOOOOOOOOO|....O.....|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 114.31,\n",
-       "  OOOOOOOOOO|..O.......|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 125.59,\n",
-       "  ........O.|OOOOOOOOOO|.O........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 100.78,\n",
-       "  ........O.|OOOOOOOOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 128.53,\n",
-       "  .........O|OOOOOOOOOO|.O........|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 125.84,\n",
-       "  OOOOOOOOOO|...O......|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 173.75,\n",
-       "  OOOOOOOOOO|...O......|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 130.46,\n",
-       "  ........O.|......O...|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 109.67,\n",
-       "  .....O....|......OOOO|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 2 fitness: 64.22,\n",
-       "  OOOOOOOOOO|.........O|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 115.79,\n",
-       "  .....O....|.........O|OOOOOOOOOO|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 97.28,\n",
-       "  OOOOOOOOOO|..O.......|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 121.75,\n",
-       "  ...O......|..O.......|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 124.63,\n",
-       "  ...O......|..O.......|OOOOOOOOOO|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 106.73,\n",
-       "  OOOOOOOO..|O.........|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 104.37,\n",
-       "  OOOOOOOOOO|O.........|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 169.83,\n",
-       "  .......O..|OOOOOOOOOO|....O.....|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 113.24,\n",
-       "  .......O..|O.........|OOOOOOOOOO|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 135.34,\n",
-       "  OOOOOOOO..|......O...|...O......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 92.04,\n",
-       "  ......O...|......O...|...O......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 23.76,\n",
-       "  ......O...|......O...|...O......|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 14.75,\n",
-       "  ......O...|......O...|...O......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 28.38,\n",
-       "  ..O.......|.....O....|OOOOOOOOOO|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 20 fitness: 16.35,\n",
-       "  ........O.|..O.......|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 198.73,\n",
-       "  ........O.|OOOOOOOOOO|O.........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 3 fitness: 125.32,\n",
-       "  ........O.|OOOOOOOOOO|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 142.65,\n",
-       "  OOOOOOOOOO|O.........|...O......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 163.40,\n",
-       "  OOOOOOOOOO|...O......|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 2 fitness: 160.09,\n",
-       "  ........O.|......O...|......O...|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 2 fitness: 71.19,\n",
-       "  ........O.|OOOOOOOOOO|..O.......|.OOOOOO...\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.....OOOOO x 4 fitness: 263.64,\n",
-       "  ........O.|.O........|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 120.56,\n",
-       "  .........O|......OOOO|....O.....|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOOO x 3 fitness: 69.70,\n",
-       "  .........O|OOOOOOOOOO|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 3 fitness: 109.71,\n",
-       "  OOOOOOOOOO|..O.......|.O........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 206.67,\n",
-       "  .........O|OOOOOOO...|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 113.64,\n",
-       "  .........O|OOOOOOOOOO|.O........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 108.06,\n",
-       "  ...O......|OOOOOOOOOO|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 105.02,\n",
-       "  OOOOOOOOOO|..O.......|O.........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 157.00,\n",
-       "  ...O......|..O.......|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 108.27,\n",
-       "  OOOOOOOO..|.O........|........O.|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 103.54,\n",
-       "  OOOOOOOOOO|.O........|........O.|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 135.76,\n",
-       "  ..O.......|.O........|OOOOOOOOOO|.OOO......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 3 fitness: 224.50,\n",
-       "  OOOOOOOOOO|....O.....|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 186.22,\n",
-       "  OOOOOOOOOO|....O.....|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 139.02,\n",
-       "  ..O.......|..OOOOOOOO|.O........|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 87.10,\n",
-       "  ....O.....|....O.....|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 111.64,\n",
-       "  OOOOOOOOOO|....O.....|...O......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|....OOOO.. x 1 fitness: 155.48,\n",
-       "  OOOOOOOOOO|....O.....|...O......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 142.23,\n",
-       "  OOOOOOOO..|....O.....|...O......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 109.20,\n",
-       "  ....O.....|..OOOOOOOO|...O......|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 108.10,\n",
-       "  .......O..|OOOOOOOOOO|......O...|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 16 fitness: 26.78,\n",
-       "  ....O.....|.....O....|OOOOOOOOOO|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 11 fitness: 19.23,\n",
-       "  ......O...|.......O..|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 111.27,\n",
-       "  ......O...|......OOOO|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 78.04,\n",
-       "  OOOOOOOOOO|O.........|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 151.77,\n",
-       "  ...O......|OOOOOOOOOO|.......O..|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 107.58,\n",
-       "  ...O......|..O.......|.......O..|OOO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 35.21,\n",
-       "  .O........|....O.....|......O...|OO........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 31.58,\n",
-       "  .O........|....O.....|......O...|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........O. x 1 fitness: 19.61,\n",
-       "  .O........|....O.....|......OOOO|OO........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOO. x 11 fitness: 78.32,\n",
-       "  OOOOOOOOOO|......O...|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 75.95,\n",
-       "  .......O..|OOOOOOOOOO|.O........|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 7 fitness: 212.57,\n",
-       "  OOOOOOOOOO|.....O....|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 126.46,\n",
-       "  ........O.|OOOOOOOOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 2 fitness: 184.10,\n",
-       "  ..O.......|..O.......|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 298.38,\n",
-       "  OOOOOOOOOO|....O.....|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 1 fitness: 186.03,\n",
-       "  ..O.......|....O.....|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 94.23,\n",
-       "  ..O.......|OOOOOOOOOO|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 128.65,\n",
-       "  ..O.......|....O.....|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOOO x 7 fitness: 198.84,\n",
-       "  ....O.....|..O.......|......O...|OO........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 30.88,\n",
-       "  ....O.....|..O.......|......O...|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 23.45,\n",
-       "  ....O.....|OOO.......|......O...|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........O. x 2 fitness: 35.56,\n",
-       "  ....O.....|..O.......|......OOOO|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 6 fitness: 99.68,\n",
-       "  ..OOOOOOOO|.......O..|......O...|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 43.57,\n",
-       "  .O........|OOOOOOOOOO|.O........|..OOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 84.07,\n",
-       "  .O........|OOOOOOOOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 93.67,\n",
-       "  .........O|.....O....|......O...|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 44.71,\n",
-       "  OOOOOOOO..|.....O....|...O......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 99.50,\n",
-       "  .........O|OOOOOOOOOO|........O.|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 19 fitness: 38.09,\n",
-       "  ......O...|...O......|......O...|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 2 fitness: 40.04,\n",
-       "  OOOOOOOOOO|.....O....|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 147.52,\n",
-       "  ......OOOO|OOOOOOOOOO|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 19 fitness: 370.76,\n",
-       "  .....O....|......OOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 77.16,\n",
-       "  .....O....|OOOOOOOOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 4 fitness: 152.34,\n",
-       "  .........O|..O.......|..O.......|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 22.71,\n",
-       "  .........O|OOOOOOOOOO|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 3 fitness: 191.13,\n",
-       "  .........O|OOOOOOOOOO|..O.......|OOO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 18 fitness: 337.96,\n",
-       "  ...O......|.......O..|OOOOOOOOOO|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 4 fitness: 47.48,\n",
-       "  ........O.|OOOOOOOOOO|...O......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 142.41,\n",
-       "  ....O.....|OOOOOOOOOO|........O.|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 109.34,\n",
-       "  ....O.....|OOOOO.....|........O.|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 8 fitness: 115.00,\n",
-       "  O.........|..O.......|.........O|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 17.64,\n",
-       "  O.........|..O.......|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 95.80,\n",
-       "  O.........|..O.......|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 6 fitness: 198.25,\n",
-       "  OOOOOOOOOO|.O........|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 111.37,\n",
-       "  OOOOOOOOOO|O.........|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 126.84,\n",
-       "  .........O|O.........|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 128.15,\n",
-       "  .O........|.......O..|......OOOO|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 75.20,\n",
-       "  OOOOOOOOOO|...O......|...O......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 138.46,\n",
-       "  OOOOOOOO..|...O......|...O......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 109.65,\n",
-       "  ......O...|...O......|.OOOOOOOOO|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 4 fitness: 90.91,\n",
-       "  ......O...|OOOO......|...O......|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 81.82,\n",
-       "  .........O|......OOOO|....O.....|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 8 fitness: 114.82,\n",
-       "  .........O|.......O..|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 110.74,\n",
-       "  OOOOOOOOOO|........O.|...O......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 130.59,\n",
-       "  OOOOOOOOOO|........O.|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 126.97,\n",
-       "  ........O.|OOOOOOOOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 7 fitness: 248.39,\n",
-       "  OOOOOOOOOO|.........O|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 114.92,\n",
-       "  OOOOOOOO..|O.........|.O........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 105.45,\n",
-       "  OOOOOOOOOO|O.........|.O........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 142.46,\n",
-       "  OOOOOOOO..|.O........|.......O..|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 101.89,\n",
-       "  O.........|.O........|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 107.92,\n",
-       "  O.........|OOOOOOOOOO|.......O..|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 98.82,\n",
-       "  O.........|.O........|OOOOOOOOOO|.OOO......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 1 fitness: 249.05,\n",
-       "  O.........|.O........|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 8 fitness: 180.02,\n",
-       "  OOOOOOOOOO|..O.......|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 150.30,\n",
-       "  .O........|.........O|.........O|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 59.33,\n",
-       "  .........O|OOOOOOOOOO|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 148.90,\n",
-       "  .........O|..O.......|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 85.97,\n",
-       "  ........O.|..O.......|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 77.52,\n",
-       "  ........O.|..O.......|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 82.76,\n",
-       "  ........O.|......O...|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 96.60,\n",
-       "  OOOOOOOOOO|....O.....|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|....OOOO.. x 1 fitness: 132.39,\n",
-       "  ........O.|OOOOOOOOOO|O.........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 148.74,\n",
-       "  OOOOOOOOOO|....O.....|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|....OOOO.. x 6 fitness: 227.77,\n",
-       "  ....O.....|....O.....|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 24.02,\n",
-       "  .........O|.........O|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 106.26,\n",
-       "  .........O|....OOOOOO|...O......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OO. x 5 fitness: 144.74,\n",
-       "  .........O|OOOOOOOOOO|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 105.43,\n",
-       "  .........O|.......O..|.......O..|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 2 fitness: 83.22,\n",
-       "  ........O.|OOOOOOOOOO|......O...|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 19 fitness: 19.98,\n",
-       "  ........O.|........O.|......O...|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 73.10,\n",
-       "  OOOOOOOOOO|..O.......|.......O..|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 159.23,\n",
-       "  .........O|.O........|OOOOOOOOOO|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 140.56,\n",
-       "  OOOOOOOOOO|.O........|..O.......|.OOOOOO...\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.....OOOOO x 4 fitness: 231.23,\n",
-       "  .........O|.O........|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 3 fitness: 141.28,\n",
-       "  ....O.....|.........O|....O.....|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 2 fitness: 35.63,\n",
-       "  O.........|OOOOOOOOOO|....O.....|..OO......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........O. x 1 fitness: 80.52,\n",
-       "  O.........|OOOOOOOOOO|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 132.30,\n",
-       "  O.........|....O.....|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 161.93,\n",
-       "  O.........|....O.....|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 101.69,\n",
-       "  O.........|....O.....|....OOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 7 fitness: 161.59,\n",
-       "  O.........|....O.....|OOOOO.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 59.40,\n",
-       "  OOOOO.....|......O...|OOOOOOOOOO|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 9 fitness: 0.00,\n",
-       "  OOOOOOOO..|O.........|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 96.66,\n",
-       "  ....O.....|OOOOOOOOOO|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 148.30,\n",
-       "  OOOOOOOOOO|O.........|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 124.32,\n",
-       "  .........O|....O.....|....OOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 39.58,\n",
-       "  .........O|....O.....|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 102.18,\n",
-       "  ..O.......|.......O..|..O.......|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 74.18,\n",
-       "  O.........|........O.|.........O|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 42.41,\n",
-       "  .O........|....O.....|........O.|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 17.14,\n",
-       "  .O........|....O.....|........O.|..OOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 37.18,\n",
-       "  .O........|....O.....|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|....OOOO.. x 2 fitness: 222.44,\n",
-       "  OOOOO.....|....O.....|........O.|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 6 fitness: 108.52,\n",
-       "  OOOOOOOOOO|.O........|.........O|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 119.69,\n",
-       "  .........O|......OOOO|O.........|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOOO x 4 fitness: 83.28,\n",
-       "  .........O|OOOOOOOOOO|O.........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 149.47,\n",
-       "  ......O...|OOOOOOOOOO|.....O....|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 16 fitness: 27.81,\n",
-       "  OOOOOOOOOO|...O......|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 133.50,\n",
-       "  .......O..|..OOOOOOOO|....O.....|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 158.89,\n",
-       "  ........O.|..O.......|.........O|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 43.81,\n",
-       "  .O........|..O.......|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 159.01,\n",
-       "  OOOOOOOOOO|..O.......|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 128.29,\n",
-       "  ...O......|...O......|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 110.16,\n",
-       "  ...O......|OOOOOOOOOO|...O......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 112.31,\n",
-       "  OOOOOOOO..|.O........|O.........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 103.06,\n",
-       "  ......O...|OOOOOOOOOO|O.........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 149.81,\n",
-       "  ......O...|OOOOOOOOOO|O.........|OO........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 6 fitness: 274.42,\n",
-       "  .........O|OOOOOOOOOO|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 170.38,\n",
-       "  OOOOOOOO..|O.........|......O...|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 93.49,\n",
-       "  ........O.|........O.|.O........|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 23.28,\n",
-       "  ........O.|.....O....|O.........|OOO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 35.50,\n",
-       "  OOOOOOOOOO|..O.......|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 176.32,\n",
-       "  ......O...|OOOOOOOOOO|..O.......|.OOOOOO...\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.....OOOOO x 2 fitness: 281.58,\n",
-       "  ......O...|..O.......|.OOOOOOOOO|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 86.66,\n",
-       "  ........O.|OOOOOOOOOO|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 3 fitness: 122.41,\n",
-       "  ........O.|OOOOOOOOOO|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 116.35,\n",
-       "  .......O..|..OOOOOOOO|O.........|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 196.22,\n",
-       "  ........O.|OOOOOOOOOO|...O......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 103.05,\n",
-       "  ........O.|....OOOOOO|...O......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OO. x 12 fitness: 103.85,\n",
-       "  ......O...|OOOOOOOOOO|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 107.28,\n",
-       "  ......O...|...O......|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 35.12,\n",
-       "  .O........|..O.......|.........O|..OOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 26.39,\n",
-       "  .......O..|OOOOOOOOOO|........O.|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 15 fitness: 36.48,\n",
-       "  ......O...|OOOOOOOOOO|.......O..|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 24.49,\n",
-       "  ......O...|......O...|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 118.46,\n",
-       "  ......O...|..OOOOOOOO|..O.......|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 154.85,\n",
-       "  ......O...|......O...|..O.......|OO........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 47.50,\n",
-       "  .......O..|OOOOOOOOOO|.........O|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 18 fitness: 4.90,\n",
-       "  .........O|....O.....|.....O....|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 73.43,\n",
-       "  .....O....|........O.|...O......|OO........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 27.76,\n",
-       "  .....O....|......OOOO|...O......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 79.53,\n",
-       "  OOOOOOOO..|........O.|...O......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 102.35,\n",
-       "  ......O...|OOOOOOOOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 3 fitness: 157.30,\n",
-       "  ......O...|........O.|.O........|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 30.50,\n",
-       "  .........O|OOOOOOOOOO|.......O..|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 18 fitness: 24.55,\n",
-       "  OOOOOOOOOO|....O.....|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 199.45,\n",
-       "  ......O...|....O.....|....O.....|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 24.85,\n",
-       "  .......O..|OOOOOOOOOO|....O.....|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 6 fitness: 210.77,\n",
-       "  .......O..|......OOOO|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 3 fitness: 102.40,\n",
-       "  .......O..|..OOOOOOOO|....O.....|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|...OOOOOOO x 17 fitness: 230.01,\n",
-       "  ........O.|....O.....|........O.|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 85.70,\n",
-       "  ..O.......|..O.......|.........O|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 15.00,\n",
-       "  ..O.......|..O.......|OOOOOOOOOO|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......O... x 3 fitness: 101.31,\n",
-       "  ...O......|..OOOOOOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........O. x 1 fitness: 94.05,\n",
-       "  O.........|..O.......|OOOOOOOOO.|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 4 fitness: 187.76,\n",
-       "  .O........|..O.......|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 184.14,\n",
-       "  .O........|..O.......|.........O|..OOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 29.21,\n",
-       "  OOOOOOOOOO|..O.......|......O...|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 186.47,\n",
-       "  ...O......|..O.......|......O...|OO........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 32.99,\n",
-       "  .........O|OOOOOOOOOO|.........O|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 19 fitness: 5.01,\n",
-       "  O.........|.....O....|OOOOOOOOOO|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 19 fitness: 29.31,\n",
-       "  O.........|.....O....|..O.......|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 93.10,\n",
-       "  ...O......|........O.|........O.|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 38.12,\n",
-       "  ..OOO.....|O.........|OOOOOOOOOO|.OOOOOO...\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 10 fitness: 402.74,\n",
-       "  OOOOOOOOOO|..O.......|...O......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 180.63,\n",
-       "  .........O|OOOOOOO...|...O......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 108.01,\n",
-       "  .........O|OOOOOOOOOO|...O......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 113.38,\n",
-       "  ...O......|.........O|..O.......|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 2 fitness: 29.96,\n",
-       "  .......O..|......OOOO|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 72.89,\n",
-       "  .......O..|......O...|O.........|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 25.48,\n",
-       "  ..O.......|......O...|..O.......|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 75.53,\n",
-       "  O.........|OO........|.....O....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 32.08,\n",
-       "  O.........|.O........|OOOOOOOOO.|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 6 fitness: 191.34,\n",
-       "  ..O.......|..O.......|.........O|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 18.06,\n",
-       "  ........O.|.O........|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 14.74,\n",
-       "  ........O.|.O........|OOOOOOO...|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........O. x 3 fitness: 113.68,\n",
-       "  ........O.|OOOOOOOOOO|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 95.88,\n",
-       "  ....O.....|...O......|......OOOO|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 8 fitness: 98.37,\n",
-       "  OOOOO.....|...O......|........O.|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 101.54,\n",
-       "  OOOOO.....|OOOOO.....|........O.|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOOO x 15 fitness: 243.24,\n",
-       "  O.........|..O.......|OOOOO.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 64.99,\n",
-       "  .O........|.........O|....O.....|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 36.84,\n",
-       "  ........O.|........O.|....O.....|.OOO......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 1 fitness: 34.33,\n",
-       "  .........O|..O.......|.....O....|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 36.49,\n",
-       "  .O........|....O.....|..O.......|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 3 fitness: 25.82,\n",
-       "  O.........|....O.....|O.........|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 24.71,\n",
-       "  O.........|....O.....|OOOOO.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 59.22,\n",
-       "  O.........|....O.....|OOOOOOOOO.|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 9 fitness: 184.86,\n",
-       "  OOOOOOOOOO|..O.......|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 2 fitness: 163.70,\n",
-       "  OOOOOOOOOO|....O.....|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 193.31,\n",
-       "  OOOOOOOOOO|..O.......|.....O....|OOO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOOO x 1 fitness: 52.96,\n",
-       "  OOOOOOOOOO|.O........|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 1 fitness: 145.82,\n",
-       "  OOOOOOOOOO|.O........|.O........|.OOO......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 2 fitness: 191.74,\n",
-       "  .....O....|..O.......|.OOOOOOOOO|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOO. x 6 fitness: 99.34,\n",
-       "  ..O.......|......O...|.....O....|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 60.97,\n",
-       "  ........O.|..O.......|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 112.43,\n",
-       "  ....O.....|OOOOOOOOOO|..O.......|.OOOOOO...\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.....OOOOO x 1 fitness: 125.35,\n",
-       "  ........O.|OOOOOOOOOO|O.........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 115.04,\n",
-       "  ...O......|........O.|.O........|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 100.99,\n",
-       "  ....O.....|.O........|.....O....|OOO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 3 fitness: 55.32,\n",
-       "  ....O.....|.O........|OOOOOOOOOO|.OOO......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 3 fitness: 207.76,\n",
-       "  ....O.....|.O........|.....O....|OOO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOOO x 1 fitness: 42.19,\n",
-       "  ....O.....|........O.|.O........|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 2 fitness: 78.51,\n",
-       "  O.........|..O.......|.........O|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 5 fitness: 23.32,\n",
-       "  O.........|..O.......|.........O|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 23.53,\n",
-       "  O.........|..O.......|.OOOOOOOOO|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOO. x 5 fitness: 224.98,\n",
-       "  ..O.......|....O.....|..OOOOOOOO|..OOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 6 fitness: 169.97,\n",
-       "  ..OOOOOOOO|....O.....|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 164.09,\n",
-       "  OOO.......|....O.....|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 14 fitness: 323.14,\n",
-       "  .O........|O.........|......OOOO|OO........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOO. x 1 fitness: 62.63,\n",
-       "  .......O..|OOOOOOOOOO|......O...|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 17 fitness: 27.41,\n",
-       "  .........O|.........O|OOOOOOOOOO|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 98.92,\n",
-       "  OOOOOOOOOO|..O.......|..O.......|.OOOOOO...\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.....OOOOO x 1 fitness: 201.56,\n",
-       "  ...O......|..O.......|..O.......|.OOOOOO...\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.....OOOOO x 2 fitness: 96.26,\n",
-       "  ...O......|..O.......|.OOOOOOOOO|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOO. x 10 fitness: 246.75,\n",
-       "  ......O...|.O........|.........O|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 51.51,\n",
-       "  ......O...|..OOOOOOOO|.O........|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 2 fitness: 146.43,\n",
-       "  ......O...|..OOOOOOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........O. x 2 fitness: 159.45,\n",
-       "  .O........|...O......|..O.......|..OOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 54.45,\n",
-       "  .O........|...O......|..O.......|OOOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 51.34,\n",
-       "  OOOOOOOOOO|....O.....|.........O|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 147.75,\n",
-       "  .O........|....O.....|....OOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 2 fitness: 140.89,\n",
-       "  .O........|....O.....|.........O|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 17.97,\n",
-       "  .O........|....O.....|.........O|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOOO x 2 fitness: 32.86,\n",
-       "  O.........|.O........|........O.|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 19.24,\n",
-       "  OOOOO.....|.O........|........O.|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 3 fitness: 110.08,\n",
-       "  ....O.....|.O........|.......O..|OOO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 36.77,\n",
-       "  ....O.....|.O........|.......O..|OOO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 36.96,\n",
-       "  ........O.|.......O..|.........O|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 65.58,\n",
-       "  ....O.....|....O.....|...O......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.....OOOOO x 1 fitness: 39.09,\n",
-       "  ....O.....|....O.....|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 182.83,\n",
-       "  .....O....|....O.....|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 148.62,\n",
-       "  ....O.....|........O.|.........O|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 78.70,\n",
-       "  .O........|O.........|.........O|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 23.40,\n",
-       "  .O........|O.........|.........O|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOOO x 1 fitness: 40.13,\n",
-       "  ......O...|.O........|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 33.35,\n",
-       "  ......OOOO|.O........|.O........|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 4 fitness: 89.21,\n",
-       "  .........O|..O.......|.........O|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 2 fitness: 68.94,\n",
-       "  .........O|OOOOOOO...|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 115.68,\n",
-       "  OOOOOOOOOO|..O.......|....O.....|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 6 fitness: 272.10,\n",
-       "  OOOOOOOOOO|..O.......|OO........|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 6 fitness: 240.24,\n",
-       "  .......O..|..OOOOOOOO|O.........|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 13 fitness: 192.85,\n",
-       "  .......O..|........O.|.....O....|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 15.33,\n",
-       "  .........O|........O.|..O.......|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 23.36,\n",
-       "  .........O|......OOOO|..O.......|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 3 fitness: 100.49,\n",
-       "  ......O...|OOOOOOOOOO|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 3 fitness: 159.83,\n",
-       "  ......O...|.O........|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 34.19,\n",
-       "  ..O.......|....O.....|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 23.56,\n",
-       "  .........O|OOOOOOOOOO|......O...|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 17 fitness: 13.24,\n",
-       "  ......O...|....O.....|........O.|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 65.94,\n",
-       "  ...O......|....O.....|........O.|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 21.24,\n",
-       "  ...O......|....O.....|....OOOOOO|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOO. x 2 fitness: 48.36,\n",
-       "  ...O......|........O.|.O........|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 71.46,\n",
-       "  ......O...|......OOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 3 fitness: 86.82,\n",
-       "  .........O|.........O|..O.......|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 3 fitness: 16.24,\n",
-       "  ......O...|.......O..|....O.....|OOO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 2 fitness: 34.64,\n",
-       "  ..O.......|..O.......|.....O....|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 22.01,\n",
-       "  .......O..|..O.......|.........O|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 52.77,\n",
-       "  ..O.......|.O........|........O.|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOOO x 1 fitness: 33.42,\n",
-       "  ..O.......|.O........|..OOOOOOOO|..OOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 2 fitness: 189.10,\n",
-       "  ........O.|.O........|.......O..|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 75.71,\n",
-       "  .........O|OOOOOOOOOO|O.........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 4 fitness: 146.81,\n",
-       "  ........O.|......O...|..O.......|OO........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 28.60,\n",
-       "  ........O.|......O...|..O.......|.OOOOOO...\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.....OOOOO x 2 fitness: 84.19,\n",
-       "  .O........|....O.....|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.....OOOOO x 1 fitness: 38.51,\n",
-       "  .O........|....O.....|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 3 fitness: 212.50,\n",
-       "  ....O.....|....O.....|.O........|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.....OOOOO x 1 fitness: 29.80,\n",
-       "  ....O.....|..OOOOOOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........O. x 1 fitness: 109.09,\n",
-       "  ....O.....|.........O|OOOOOOOOOO|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 25.48,\n",
-       "  ....O.....|.........O|....O.....|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 38.57,\n",
-       "  ....O.....|..OOOOOOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........O. x 2 fitness: 79.63,\n",
-       "  OOOOOOOOOO|O.........|..O.......|.OOOOOO...\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.....OOOOO x 1 fitness: 213.52,\n",
-       "  .........O|OO........|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 30.00,\n",
-       "  OOOOOOOOOO|.O........|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOOO x 1 fitness: 185.73,\n",
-       "  .........O|..O.......|.........O|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 59.48,\n",
-       "  .O........|..OOOOOOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........O. x 1 fitness: 89.57,\n",
-       "  .O........|..O.......|.O........|..OOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 35.89,\n",
-       "  OOOOOOOOOO|..O.......|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOOO x 1 fitness: 229.09,\n",
-       "  OOOOOOOOOO|OOOOOOOOOO|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOOO x 2 fitness: 238.62,\n",
-       "  .O........|..O.......|.....O....|OOO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 40.21,\n",
-       "  OOOOOOOOOO|....O.....|......O...|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 119.82,\n",
-       "  .O........|....O.....|......O...|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 21.03,\n",
-       "  ....O.....|O.........|.......O..|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 11.90,\n",
-       "  OOOOOOOOOO|O.........|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOOO x 2 fitness: 244.87,\n",
-       "  ....O.....|..O.......|.OOOOOOOOO|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 4 fitness: 195.97,\n",
-       "  ...O......|.........O|........O.|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 53.18,\n",
-       "  O.........|O.........|OOOOOOOOO.|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 3 fitness: 166.34,\n",
-       "  .O........|.....O....|......O...|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 93.12,\n",
-       "  ...O......|....O.....|..O.......|.OOOOOO...\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.....OOOOO x 1 fitness: 100.30,\n",
-       "  .O........|.........O|....O.....|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 74.94,\n",
-       "  ..O.......|.........O|....O.....|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 36.90,\n",
-       "  .O........|...O......|........O.|..OOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 21.74,\n",
-       "  ......O...|..OOOOOOOO|.O........|.OOOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 119.08,\n",
-       "  OOOOOOO...|....O.....|.O........|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 6 fitness: 194.05,\n",
-       "  ...O......|....O.....|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 218.87,\n",
-       "  ...O......|....O.....|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 9 fitness: 222.60,\n",
-       "  ..O.......|.........O|.O........|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 29.88,\n",
-       "  .........O|OOOOOOO...|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 107.76,\n",
-       "  .........O|......O...|....O.....|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 1 fitness: 21.96,\n",
-       "  .........O|OOOOOOOO..|OOOOO.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 4 fitness: 276.84,\n",
-       "  .....O....|........O.|......O...|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 56.95,\n",
-       "  OOOOOOOOOO|.O........|......O...|.OOO......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 1 fitness: 122.70,\n",
-       "  OOOOOOOOOO|..O.......|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOOO x 1 fitness: 99.39,\n",
-       "  .......O..|OOOOOOOO..|OOOOO.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 9 fitness: 284.56,\n",
-       "  OOOO......|.O........|O.........|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOOO x 2 fitness: 53.42,\n",
-       "  OOOOOOOOOO|.O........|O.........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOOO x 1 fitness: 79.95,\n",
-       "  .........O|OOOOOOOO..|OOOOO.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 6 fitness: 255.96,\n",
-       "  ..O.......|......O...|.O........|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 84.53,\n",
-       "  .O........|..O.......|....O.....|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 26.26,\n",
-       "  .O........|..O.......|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOOO x 3 fitness: 122.46,\n",
-       "  O.........|........O.|....O.....|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 80.23,\n",
-       "  .........O|OOOOOOO...|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 4 fitness: 110.15,\n",
-       "  .........O|..OOOOOOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........O. x 1 fitness: 138.25,\n",
-       "  OOOOOOOOOO|..O.......|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOOO x 1 fitness: 121.79,\n",
-       "  ..O.......|.......OOO|OOOOOOOOOO|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 19 fitness: 0.15,\n",
-       "  .....O....|..OOOOOOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........O. x 2 fitness: 84.74,\n",
-       "  .........O|OOOOOOOO..|.......O..|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 74.01,\n",
-       "  ..O.......|O.........|..OOOOOOOO|..OOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 2 fitness: 149.61,\n",
-       "  ......O...|....O.....|.........O|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 55.53,\n",
-       "  .........O|....O.....|....O.....|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.....OOOOO x 1 fitness: 27.69,\n",
-       "  .........O|....O.....|....O.....|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 15.29,\n",
-       "  ...O......|O.........|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 5 fitness: 188.11,\n",
-       "  ....O.....|.......OOO|OOOOOOOOOO|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 18 fitness: 0.22,\n",
-       "  .........O|.O........|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOOO x 1 fitness: 79.09,\n",
-       "  ..O.......|..O.......|.......O..|OOO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 37.98,\n",
-       "  ..O.......|..O.......|..OOOOOOOO|..OOO.....\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 143.71,\n",
-       "  ..OOOOOOOO|....O.....|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 127.48,\n",
-       "  .....O....|.O........|.......O..|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 2 fitness: 65.99,\n",
-       "  .......O..|O.........|.O........|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 22.01,\n",
-       "  ...O......|.O........|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 176.83,\n",
-       "  O.........|........O.|.O........|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 69.07,\n",
-       "  ...O......|...O......|........O.|OO........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 35.68,\n",
-       "  ..O.......|........O.|.........O|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 49.20,\n",
-       "  O.........|.O........|O.........|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 5 fitness: 36.84,\n",
-       "  O.........|.O........|OOOOO.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 52.12,\n",
-       "  O.........|.O........|OOOOOOOOO.|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 3 fitness: 193.84,\n",
-       "  .........O|..OOOOOOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........O. x 1 fitness: 111.20,\n",
-       "  OOOOOOOOOO|.......OOO|.....O....|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 3 fitness: 0.02,\n",
-       "  .........O|..O.......|..O.......|..O.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 26.16,\n",
-       "  .........O|OOOOOOOO..|.....O....|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 2 fitness: 42.83,\n",
-       "  ..O.......|.......OOO|OOOOOOOOOO|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 18 fitness: 0.05,\n",
-       "  .O........|.....O....|OOOOOO....|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 2 fitness: 19.61,\n",
-       "  OOO.......|.O........|......O...|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 4 fitness: 61.77,\n",
-       "  .O........|.O........|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOOO x 1 fitness: 103.77,\n",
-       "  .......O..|.......OOO|OOOOOOOOOO|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 121.44,\n",
-       "  .O........|O.........|......O...|OO........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 22.56,\n",
-       "  .O........|O.........|......O...|OO........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 32.47,\n",
-       "  OOOOOOOOOO|O.........|......O...|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOOO x 2 fitness: 140.01,\n",
-       "  ......O...|OOOOOOOOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 135.20,\n",
-       "  ......O...|..OOOOOOOO|.O........|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........O. x 2 fitness: 120.11,\n",
-       "  ......O...|..O.......|OOOOOOOOOO|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOOO x 1 fitness: 125.13,\n",
-       "  OOOOOOOOOO|..O.......|...O......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOOO x 1 fitness: 142.78,\n",
-       "  ......O...|......O...|......O...|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 2 fitness: 50.93,\n",
-       "  O.........|....O.....|.........O|..OO......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........O. x 2 fitness: 27.95,\n",
-       "  O.........|....O.....|.........O|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 15.26,\n",
-       "  O.........|....O.....|.........O|OO........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 20.65,\n",
-       "  ......O...|OO........|..O.......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 33.63,\n",
-       "  .....O....|....O.....|...O......|.O........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.....OOOOO x 1 fitness: 36.31,\n",
-       "  .....O....|OOOOOOOOOO|...O......|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 2 fitness: 97.94,\n",
-       "  ..OOOOOOOO|....O.....|...O......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 1 fitness: 125.65,\n",
-       "  OOOOOOOOOO|O.........|......O...|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOOO x 1 fitness: 131.11,\n",
-       "  ........O.|.....O....|......O...|OOOOOOOOOO\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO x 1 fitness: 57.20,\n",
-       "  ...O......|..O.......|......O...|O.........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 3 fitness: 26.98,\n",
-       "  ...O......|OOOOOOOOOO|......O...|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 1 fitness: 123.29,\n",
-       "  OOOOOOOOOO|..O.......|......O...|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOOO x 1 fitness: 137.05,\n",
-       "  OOOOOOOOOO|...O......|..O.......|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|......OOOO x 1 fitness: 120.62,\n",
-       "  ..O.......|.O........|OOOOOOOOOO|.OOO......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.......OOO x 6 fitness: 244.92,\n",
-       "  .O........|..O.......|...O......|OO........\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|.........O x 2 fitness: 22.62,\n",
-       "  ..OOOOOOOO|....O.....|....O.....|.OO.......\t0\tOOOOOOOOOO|OOOOOOOOOO|OOOOOOOOOO|........OO x 2 fitness: 114.49,\n",
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-       "  ...])"
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-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
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-    {
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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "#%%time\n",
-    "evaluate(rmpx, encoder_bits=4, trials=40_000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# %%time\n",
-    "# evaluate(rmpx, encoder_bits=5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 6-bit rMPX"
+    "# Top 10 with some interval spread\n",
+    "sorted(generic_ones, key=lambda cl: -cl.fitness)[:10]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "rmpx6 = gym.make('real-multiplexer-6bit-v0')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:lcs.agents.Agent:{'trial': 0, 'steps_in_trial': 1, 'reward': 1000, 'population': 1, 'numerosity': 1, 'reliable': 0}\n",
-      "INFO:lcs.agents.Agent:{'trial': 5000, 'steps_in_trial': 1, 'reward': 1000, 'population': 2984, 'numerosity': 3877, 'reliable': 36}\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 8min, sys: 546 ms, total: 8min\n",
-      "Wall time: 8min\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "([..O.|...O|.O..|..O.|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 40.13,\n",
-       "  .O..|O...|.O..|...O|.O..|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 49.22,\n",
-       "  .O..|...O|.O..|O...|O...|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 54.49,\n",
-       "  ...O|O...|...O|...O|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 65.87,\n",
-       "  ...O|..O.|..O.|...O|...O|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 59.95,\n",
-       "  ...O|O...|..O.|.O..|...O|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 71.36,\n",
-       "  O...|...O|...O|O...|...O|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 82.92,\n",
-       "  ..O.|..O.|..O.|.O..|O...|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 90.97,\n",
-       "  .O..|..O.|..O.|...O|...O|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 93.41,\n",
-       "  ..O.|..O.|O...|O...|...O|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 85.31,\n",
-       "  .O..|...O|...O|.O..|..O.|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 80.09,\n",
-       "  O...|...O|O...|...O|..O.|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 103.21,\n",
-       "  O...|...O|O...|O...|O...|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 87.86,\n",
-       "  O...|...O|O...|..O.|..O.|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 85.33,\n",
-       "  .O..|...O|..O.|O...|...O|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 85.39,\n",
-       "  .O..|..O.|.O..|...O|...O|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 97.74,\n",
-       "  ..O.|..O.|..O.|..O.|...O|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 84.30,\n",
-       "  ..O.|..O.|...O|...O|...O|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 83.05,\n",
-       "  ..O.|O...|...O|O...|O...|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 77.92,\n",
-       "  ..O.|..O.|O...|O...|.O..|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 64.03,\n",
-       "  ...O|...O|.O..|..O.|...O|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 65.31,\n",
-       "  O...|.O..|.O..|.O..|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 61.47,\n",
-       "  ..O.|...O|..O.|...O|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 93.66,\n",
-       "  ..O.|...O|OOOO|OOOO|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 6 fitness: 549.44,\n",
-       "  .O..|..O.|O...|.O..|O...|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 68.71,\n",
-       "  ..O.|.O..|...O|..O.|O...|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 81.41,\n",
-       "  ..O.|...O|O...|..O.|...O|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 72.75,\n",
-       "  .O..|...O|OOOO|...O|OOOO|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 17 fitness: 17.95,\n",
-       "  ...O|..O.|...O|O...|O...|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 71.98,\n",
-       "  ..O.|...O|...O|.O..|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 62.12,\n",
-       "  O...|.O..|O...|...O|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 56.64,\n",
-       "  .O..|O...|.O..|..O.|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 69.56,\n",
-       "  .O..|.O..|O...|..O.|O...|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 70.33,\n",
-       "  .O..|OOOO|..O.|...O|OOOO|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 26.89,\n",
-       "  ...O|O...|..O.|...O|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 53.09,\n",
-       "  ...O|...O|..O.|O...|...O|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 47.36,\n",
-       "  O...|O...|O...|..O.|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 60.95,\n",
-       "  .O..|.O..|..O.|O...|...O|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 84.36,\n",
-       "  .O..|.O..|..O.|OOOO|OOOO|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 14 fitness: 30.38,\n",
-       "  ...O|..O.|.O..|...O|O...|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 61.29,\n",
-       "  ..O.|..O.|O...|.O..|.O..|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 66.80,\n",
-       "  .O..|O...|...O|.O..|...O|.O..|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 75.95,\n",
-       "  ..O.|.O..|.O..|O...|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 87.17,\n",
-       "  O...|O...|.O..|..O.|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 61.96,\n",
-       "  .O..|.O..|...O|..O.|O...|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 76.87,\n",
-       "  .O..|O...|.O..|O...|.O..|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 88.94,\n",
-       "  OOOO|O...|.O..|OOOO|.O..|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 4 fitness: 560.85,\n",
-       "  .O..|.O..|.O..|.O..|...O|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 88.94,\n",
-       "  .O..|.O..|...O|...O|..O.|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 85.91,\n",
-       "  ..O.|.O..|O...|...O|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 63.27,\n",
-       "  ...O|O...|...O|.O..|.O..|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 56.84,\n",
-       "  ..O.|OOOO|OOOO|OOOO|O...|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 14 fitness: 566.97,\n",
-       "  .O..|..O.|.O..|.O..|...O|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 107.75,\n",
-       "  ..O.|.O..|.O..|.O..|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 105.18,\n",
-       "  .O..|O...|...O|..O.|.O..|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 82.32,\n",
-       "  .O..|...O|.O..|.O..|O...|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 67.40,\n",
-       "  .O..|...O|OOOO|.O..|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 4 fitness: 556.50,\n",
-       "  ...O|.O..|O...|..O.|O...|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 67.24,\n",
-       "  ...O|...O|.O..|O...|.O..|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 116.41,\n",
-       "  ..O.|O...|..O.|...O|..O.|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 45.33,\n",
-       "  O...|O...|..O.|O...|..O.|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 103.54,\n",
-       "  .O..|...O|.O..|..O.|...O|.O..|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 78.44,\n",
-       "  ..O.|.O..|.O..|O...|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 79.43,\n",
-       "  ..O.|..O.|..O.|O...|O...|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 85.73,\n",
-       "  ...O|..O.|...O|..O.|.O..|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 80.31,\n",
-       "  .O..|.O..|...O|O...|...O|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 90.79,\n",
-       "  ..O.|.O..|..O.|..O.|.O..|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 86.10,\n",
-       "  ...O|.O..|O...|..O.|...O|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 98.21,\n",
-       "  ..O.|...O|..O.|..O.|.O..|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 53.40,\n",
-       "  .O..|..O.|...O|..O.|O...|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 80.76,\n",
-       "  .O..|...O|...O|.O..|O...|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 59.27,\n",
-       "  ..O.|..O.|..O.|...O|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 87.91,\n",
-       "  O...|.O..|.O..|..O.|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 58.35,\n",
-       "  ..O.|.O..|.O..|..O.|..O.|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 86.74,\n",
-       "  ...O|...O|..O.|...O|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 55.73,\n",
-       "  ...O|.O..|O...|...O|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 80.12,\n",
-       "  ..O.|.O..|.O..|...O|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 79.96,\n",
-       "  .O..|O...|...O|..O.|..O.|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 83.04,\n",
-       "  .O..|OOOO|.O..|.O..|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 10 fitness: 537.72,\n",
-       "  ...O|..O.|.O..|.O..|O...|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 79.85,\n",
-       "  ...O|...O|..O.|...O|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 94.38,\n",
-       "  ...O|...O|OOOO|...O|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 104.51,\n",
-       "  O...|..O.|..O.|.O..|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 96.44,\n",
-       "  OOOO|..O.|...O|.O..|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 83.20,\n",
-       "  .O..|...O|..O.|.O..|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 78.15,\n",
-       "  .O..|.O..|.O..|.O..|.O..|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 78.15,\n",
-       "  .O..|.O..|O...|...O|...O|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 87.10,\n",
-       "  .O..|.O..|O...|OOOO|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 11 fitness: 552.98,\n",
-       "  ...O|.O..|...O|...O|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 102.51,\n",
-       "  .O..|..O.|.O..|.O..|..O.|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 108.62,\n",
-       "  ..O.|..O.|OOOO|.O..|OOOO|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 85.44,\n",
-       "  O...|O...|.O..|O...|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 101.58,\n",
-       "  .O..|O...|..O.|OOOO|OOOO|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 67.40,\n",
-       "  .O..|O...|.O..|...O|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 88.41,\n",
-       "  ...O|.O..|.O..|..O.|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 77.60,\n",
-       "  OOOO|.O..|.O..|OOOO|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 9 fitness: 555.10,\n",
-       "  ...O|.O..|OOOO|OOOO|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 10 fitness: 548.01,\n",
-       "  ..O.|.O..|...O|..O.|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 71.62,\n",
-       "  ..O.|.O..|OOOO|OOOO|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 4 fitness: 557.68,\n",
-       "  .O..|O...|...O|O...|.O..|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 93.74,\n",
-       "  .O..|O...|.O..|...O|.O..|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 80.30,\n",
-       "  ..O.|O...|...O|...O|...O|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 90.45,\n",
-       "  ..O.|..O.|..O.|O...|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 88.40,\n",
-       "  ...O|.O..|O...|...O|..O.|.O..|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 81.63,\n",
-       "  ...O|.O..|...O|...O|.O..|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 76.40,\n",
-       "  ..O.|..O.|O...|O...|O...|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 80.04,\n",
-       "  .O..|.O..|.O..|.O..|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 82.78,\n",
-       "  .O..|O...|.O..|..O.|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 86.53,\n",
-       "  ..O.|..O.|.O..|O...|..O.|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 84.66,\n",
-       "  .O..|...O|OOOO|..O.|OOOO|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 3 fitness: 51.69,\n",
-       "  ..O.|.O..|OOOO|OOOO|.O..|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 17 fitness: 562.30,\n",
-       "  ..O.|O...|..O.|.O..|.O..|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 82.32,\n",
-       "  ...O|O...|...O|...O|O...|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 93.31,\n",
-       "  ...O|O...|OOOO|OOOO|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 14 fitness: 555.75,\n",
-       "  ..O.|..O.|..O.|..O.|..O.|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 87.52,\n",
-       "  OOOO|..O.|OOOO|..O.|OOOO|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 7 fitness: 27.54,\n",
-       "  O...|...O|...O|.O..|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 89.02,\n",
-       "  ...O|...O|O...|..O.|..O.|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 87.47,\n",
-       "  ...O|...O|O...|O...|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 97.19,\n",
-       "  OOOO|...O|OOOO|O...|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 9 fitness: 560.01,\n",
-       "  ...O|..O.|...O|...O|O...|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 100.98,\n",
-       "  O...|O...|..O.|...O|O...|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 94.10,\n",
-       "  ...O|O...|..O.|O...|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 94.17,\n",
-       "  ...O|O...|OOOO|OOOO|.O..|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 6 fitness: 526.81,\n",
-       "  .O..|...O|OOOO|...O|O...|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 68.31,\n",
-       "  O...|OOOO|O...|.O..|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 6 fitness: 556.05,\n",
-       "  .O..|..O.|.O..|...O|.O..|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 78.94,\n",
-       "  ...O|OOOO|OOOO|OOOO|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 564.08,\n",
-       "  ...O|.O..|O...|..O.|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 86.34,\n",
-       "  ..O.|...O|..O.|...O|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 71.28,\n",
-       "  .O..|...O|...O|O...|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 105.81,\n",
-       "  .O..|..O.|OOOO|.O..|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 4 fitness: 520.45,\n",
-       "  OOOO|...O|OOOO|..O.|OOOO|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 18 fitness: 38.27,\n",
-       "  .O..|..O.|OOOO|..O.|OOOO|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 19 fitness: 20.04,\n",
-       "  ..O.|.O..|O...|O...|.O..|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 111.52,\n",
-       "  .O..|O...|O...|O...|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 117.43,\n",
-       "  OOOO|O...|O...|OOOO|.O..|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 6 fitness: 538.02,\n",
-       "  .O..|...O|OOOO|O...|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 4 fitness: 520.19,\n",
-       "  OOOO|...O|OOOO|O...|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 485.33,\n",
-       "  O...|.O..|..O.|OOOO|OOOO|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 12 fitness: 38.97,\n",
-       "  O...|.O..|..O.|O...|..O.|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 106.29,\n",
-       "  ..O.|...O|O...|..O.|...O|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 77.49,\n",
-       "  ...O|O...|O...|O...|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 107.03,\n",
-       "  O...|OOOO|.O..|.O..|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 9 fitness: 562.76,\n",
-       "  ...O|.O..|...O|.O..|.O..|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 51.29,\n",
-       "  O...|O...|..O.|O...|..O.|.O..|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 73.51,\n",
-       "  OOOO|O...|..O.|OOOO|..O.|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 28.19,\n",
-       "  O...|O...|..O.|O...|OOOO|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 126.34,\n",
-       "  O...|O...|..O.|.O..|..O.|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 65.12,\n",
-       "  O...|O...|..O.|OOOO|OOOO|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 6 fitness: 10.49,\n",
-       "  ...O|...O|.O..|..O.|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 77.54,\n",
-       "  ..O.|..O.|OOOO|...O|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 252.34,\n",
-       "  O...|O...|O...|.O..|.O..|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 70.24,\n",
-       "  ..O.|OOOO|.O..|.O..|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 128.15,\n",
-       "  ..O.|O...|...O|...O|.O..|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 62.47,\n",
-       "  O...|.O..|O...|..O.|...O|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 50.71,\n",
-       "  O...|.O..|O...|OOOO|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 10 fitness: 553.32,\n",
-       "  OOOO|...O|..O.|.O..|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 284.89,\n",
-       "  ...O|..O.|..O.|..O.|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 107.82,\n",
-       "  OOOO|..O.|OOOO|OO..|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 15 fitness: 603.18,\n",
-       "  OOOO|..O.|OOOO|..O.|OOOO|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 5 fitness: 36.12,\n",
-       "  ..O.|O...|.O..|O...|..O.|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 78.72,\n",
-       "  ...O|.O..|O...|.O..|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 60.18,\n",
-       "  ..O.|.O..|..O.|...O|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 105.10,\n",
-       "  OOOO|OOOO|O...|O...|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 513.09,\n",
-       "  ...O|..O.|O...|O...|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 80.13,\n",
-       "  O...|..O.|OOOO|..O.|OOOO|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 105.52,\n",
-       "  O...|OOOO|O...|O...|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 7 fitness: 560.89,\n",
-       "  O...|...O|OOOO|OOOO|O...|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 104.77,\n",
-       "  OOOO|.O..|.O..|OOOO|.O..|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 550.59,\n",
-       "  .O..|.O..|.O..|OOOO|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 5 fitness: 538.92,\n",
-       "  OOOO|O...|O...|OOOO|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 3 fitness: 549.67,\n",
-       "  O...|..O.|O...|O...|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 92.19,\n",
-       "  ..O.|.O..|.O..|O...|.O..|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 91.43,\n",
-       "  ..O.|OOOO|.O..|O...|OOOO|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 77.74,\n",
-       "  .O..|O...|O...|..O.|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 237.99,\n",
-       "  O...|...O|O...|O...|.O..|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 59.80,\n",
-       "  O...|...O|OOOO|O...|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 276.90,\n",
-       "  .O..|..O.|...O|...O|.O..|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 73.25,\n",
-       "  O...|.O..|...O|.O..|...O|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 69.58,\n",
-       "  .O..|O...|..O.|O...|OOOO|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 3 fitness: 113.58,\n",
-       "  OOOO|...O|OOOO|OO..|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 9 fitness: 580.34,\n",
-       "  ...O|...O|...O|O...|.O..|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 86.70,\n",
-       "  ..O.|...O|...O|..O.|...O|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 61.31,\n",
-       "  ..O.|...O|...O|..O.|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 84.93,\n",
-       "  O...|...O|.O..|.O..|...O|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 80.07,\n",
-       "  O...|OOOO|...O|..O.|OOOO|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 11 fitness: 31.36,\n",
-       "  OOOO|...O|...O|.O..|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 258.51,\n",
-       "  .O..|O...|O...|O...|.O..|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 66.87,\n",
-       "  .O..|OOOO|O...|O...|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 6 fitness: 555.24,\n",
-       "  OOOO|OOOO|O...|O...|.O..|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 188.79,\n",
-       "  .O..|.O..|.O..|O...|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 85.69,\n",
-       "  ..O.|...O|O...|..O.|O...|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 98.42,\n",
-       "  ..O.|..O.|O...|...O|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 89.16,\n",
-       "  ..O.|..O.|O...|O...|...O|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 58.64,\n",
-       "  ..O.|...O|..O.|..O.|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 79.96,\n",
-       "  O...|..O.|OOOO|O...|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 4 fitness: 550.76,\n",
-       "  O...|OOOO|.O..|O...|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 6 fitness: 558.74,\n",
-       "  O...|..O.|.O..|O...|...O|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 58.29,\n",
-       "  ...O|.O..|OOOO|OOOO|.O..|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 7 fitness: 559.26,\n",
-       "  ...O|.O..|..O.|.O..|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 83.69,\n",
-       "  ...O|OOOO|OOOO|OOOO|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 14 fitness: 542.75,\n",
-       "  OOOO|...O|OOOO|..O.|OOOO|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 36.31,\n",
-       "  ..O.|...O|.O..|...O|.O..|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 85.50,\n",
-       "  ...O|...O|..O.|..O.|...O|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 84.12,\n",
-       "  OOOO|..O.|..O.|O...|..O.|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 255.76,\n",
-       "  OOOO|..O.|OOOO|O...|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 10 fitness: 556.21,\n",
-       "  ...O|..O.|..O.|O...|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 88.07,\n",
-       "  ...O|..O.|OOOO|OOOO|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 11 fitness: 547.04,\n",
-       "  .O..|..O.|.O..|...O|O...|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 60.82,\n",
-       "  O...|...O|OOOO|.O..|.O..|..OO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 112.47,\n",
-       "  ...O|...O|..O.|O...|...O|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 83.02,\n",
-       "  .O..|O...|..O.|..O.|OOOO|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 104.06,\n",
-       "  .O..|.O..|.O..|...O|O...|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 75.00,\n",
-       "  O...|..O.|.O..|O...|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 84.68,\n",
-       "  ...O|.O..|..O.|.O..|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 82.68,\n",
-       "  ...O|OOOO|OOOO|OOOO|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 4 fitness: 534.52,\n",
-       "  O...|O...|.O..|..O.|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 84.79,\n",
-       "  .O..|O...|.O..|..O.|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 91.87,\n",
-       "  O...|OOOO|..O.|..O.|OOOO|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 12 fitness: 24.74,\n",
-       "  O...|O...|.O..|OOOO|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 11 fitness: 554.91,\n",
-       "  OOOO|O...|OOOO|..O.|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 216.29,\n",
-       "  .O..|OOOO|...O|...O|OOOO|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 8 fitness: 25.35,\n",
-       "  .O..|..O.|O...|.O..|.O..|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 62.49,\n",
-       "  ..O.|OOOO|OOOO|OOOO|..O.|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 6 fitness: 19.86,\n",
-       "  ...O|OOOO|..O.|.O..|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 247.49,\n",
-       "  OOOO|..O.|OOOO|.O..|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 4 fitness: 545.89,\n",
-       "  ...O|..O.|..O.|.O..|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 89.34,\n",
-       "  ..O.|O...|OOOO|OOOO|..O.|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 4 fitness: 32.25,\n",
-       "  ...O|O...|..O.|..O.|...O|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 64.51,\n",
-       "  OOOO|.O..|..O.|OOOO|...O|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 3 fitness: 62.05,\n",
-       "  ..O.|OOOO|OOOO|OOOO|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 3 fitness: 540.42,\n",
-       "  ..O.|OOOO|O...|OOOO|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 253.17,\n",
-       "  .O..|.O..|.O..|OOOO|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 343.89,\n",
-       "  ...O|.O..|...O|OO..|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 77.35,\n",
-       "  OOOO|.O..|...O|O...|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 182.33,\n",
-       "  .O..|O...|.O..|O...|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 301.04,\n",
-       "  .O..|.O..|...O|...O|OOOO|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 71.31,\n",
-       "  .O..|OOOO|.O..|OOOO|..O.|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 3 fitness: 357.42,\n",
-       "  ..O.|O...|OOOO|OOOO|..O.|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 44.21,\n",
-       "  ...O|.O..|..O.|O...|.O..|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 85.30,\n",
-       "  .O..|OOOO|O...|.O..|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 8 fitness: 542.80,\n",
-       "  OOOO|.O..|OOOO|OO..|...O|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 60.67,\n",
-       "  ...O|...O|.O..|..O.|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 75.95,\n",
-       "  OOOO|...O|OOOO|..O.|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 149.15,\n",
-       "  ...O|..O.|.O..|.O..|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 113.80,\n",
-       "  .O..|.O..|O...|...O|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 95.09,\n",
-       "  .O..|.O..|.O..|OOOO|.O..|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 320.48,\n",
-       "  .O..|.O..|.O..|OOOO|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 3 fitness: 357.94,\n",
-       "  ...O|...O|...O|..O.|.O..|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 98.25,\n",
-       "  OOOO|O...|...O|OOOO|..O.|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 7 fitness: 25.62,\n",
-       "  .O..|...O|.O..|O...|...O|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 87.11,\n",
-       "  .O..|O...|..O.|.O..|OOOO|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 57.48,\n",
-       "  .O..|..O.|..O.|...O|..O.|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 67.60,\n",
-       "  OOOO|...O|O...|...O|OOOO|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 106.94,\n",
-       "  .O..|...O|..O.|.O..|...O|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 40.67,\n",
-       "  OOOO|...O|...O|.O..|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 343.90,\n",
-       "  ...O|..O.|OOOO|OOOO|OOOO|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 3 fitness: 35.00,\n",
-       "  OOOO|..O.|OOOO|...O|.O..|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 128.31,\n",
-       "  ...O|...O|OOOO|OOOO|OOOO|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 17 fitness: 30.75,\n",
-       "  .O..|.O..|O...|..O.|...O|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 54.52,\n",
-       "  O...|OOOO|..O.|...O|OOOO|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 26.92,\n",
-       "  .O..|OOOO|OOOO|.O..|O...|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 149.88,\n",
-       "  OOOO|..O.|.O..|..O.|..O.|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 170.09,\n",
-       "  .O..|...O|.O..|..O.|..O.|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 76.64,\n",
-       "  OOOO|...O|.O..|..O.|..O.|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 73.96,\n",
-       "  O...|...O|...O|O...|...O|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 88.61,\n",
-       "  O...|...O|OOOO|O...|...O|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 320.25,\n",
-       "  ..O.|O...|..O.|.O..|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 139.33,\n",
-       "  ...O|OOOO|OOOO|OOOO|..O.|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 34.47,\n",
-       "  O...|O...|..O.|..O.|...O|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 59.18,\n",
-       "  .O..|..O.|OOOO|...O|OOOO|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 8 fitness: 31.37,\n",
-       "  ...O|OOOO|..O.|O...|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 232.55,\n",
-       "  O...|...O|..O.|O...|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 80.74,\n",
-       "  O...|O...|O...|OOOO|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 3 fitness: 272.90,\n",
-       "  ...O|..O.|O...|OOOO|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 267.21,\n",
-       "  ...O|..O.|O...|..O.|.O..|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 53.34,\n",
-       "  .O..|O...|O...|OOOO|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 9 fitness: 514.17,\n",
-       "  OOOO|...O|..O.|OOOO|.O..|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 164.51,\n",
-       "  OOOO|...O|..O.|O...|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 122.28,\n",
-       "  .O..|..O.|OOOO|.O..|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 289.81,\n",
-       "  ...O|..O.|..O.|...O|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 73.94,\n",
-       "  O...|OOOO|..O.|OOOO|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 54.90,\n",
-       "  .O..|.O..|O...|.O..|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 108.27,\n",
-       "  .O..|OOOO|O...|.O..|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 352.80,\n",
-       "  O...|..O.|OOOO|...O|OOOO|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 3 fitness: 19.53,\n",
-       "  .O..|OOOO|OOOO|.O..|O...|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 214.54,\n",
-       "  .O..|...O|...O|..O.|...O|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 56.72,\n",
-       "  O...|O...|.O..|..O.|...O|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 72.37,\n",
-       "  OOOO|.O..|...O|OOOO|..O.|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 5 fitness: 20.64,\n",
-       "  O...|OOOO|..O.|OOOO|O...|.O..|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 125.18,\n",
-       "  O...|..O.|O...|..O.|OOOO|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 98.27,\n",
-       "  O...|..O.|OOOO|..O.|O...|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 81.72,\n",
-       "  .O..|OOOO|...O|...O|OOOO|.O..|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 113.08,\n",
-       "  ..O.|..O.|OOOO|OOOO|O...|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 116.33,\n",
-       "  ..O.|..O.|...O|OOOO|OOOO|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 93.66,\n",
-       "  .OOO|.OOO|...O|.O..|...O|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 161.18,\n",
-       "  ..O.|O...|..O.|..O.|...O|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 63.85,\n",
-       "  ..O.|O...|OOOO|.O..|..O.|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 67.34,\n",
-       "  ..O.|O...|..O.|.O..|..O.|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 89.14,\n",
-       "  ...O|.O..|O...|..O.|...O|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 56.69,\n",
-       "  OOOO|.O..|O...|.O..|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 261.91,\n",
-       "  ...O|...O|OOOO|.O..|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 300.12,\n",
-       "  ..O.|...O|..O.|..O.|O...|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 47.60,\n",
-       "  ..O.|O...|OOOO|.O..|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 337.36,\n",
-       "  .O..|O...|..O.|OOOO|OOOO|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 12 fitness: 51.09,\n",
-       "  OOOO|OOOO|.O..|...O|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 228.39,\n",
-       "  ...O|..O.|OOOO|.O..|OOOO|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 97.71,\n",
-       "  ...O|..O.|OOOO|OOOO|...O|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 104.05,\n",
-       "  OOOO|..O.|O...|O...|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 187.42,\n",
-       "  ..O.|O...|..O.|..O.|...O|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 88.65,\n",
-       "  .O..|OOOO|.O..|.O..|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 273.52,\n",
-       "  ...O|.O..|..O.|.O..|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 119.71,\n",
-       "  ...O|.O..|..O.|.OOO|O...|OO..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 117.19,\n",
-       "  .O..|.O..|O...|..O.|O...|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 99.52,\n",
-       "  OOOO|..O.|OOOO|O...|.O..|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 94.43,\n",
-       "  .O..|..O.|...O|O...|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 85.32,\n",
-       "  OOOO|.O..|OOOO|O...|O...|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 307.76,\n",
-       "  OOOO|OOOO|...O|..O.|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 132.48,\n",
-       "  ...O|...O|OOOO|..O.|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 309.08,\n",
-       "  OOOO|.O..|O...|...O|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 274.04,\n",
-       "  .O..|.O..|O...|...O|O...|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 61.58,\n",
-       "  .O..|O...|..O.|O...|.O..|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 122.57,\n",
-       "  O...|..O.|..O.|.O..|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 53.04,\n",
-       "  OOOO|...O|OOOO|O...|...O|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 249.00,\n",
-       "  .O..|.O..|O...|...O|...O|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 56.29,\n",
-       "  ..O.|O...|OOOO|...O|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 324.41,\n",
-       "  OOOO|O...|.O..|OOOO|O...|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 3 fitness: 337.59,\n",
-       "  ..O.|O...|OOOO|OOOO|O...|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 5 fitness: 247.06,\n",
-       "  O...|O...|O...|...O|.O..|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 66.16,\n",
-       "  OOOO|...O|O...|..O.|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 169.36,\n",
-       "  .O..|...O|O...|..O.|...O|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 54.66,\n",
-       "  O...|...O|OOOO|...O|OOOO|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 8 fitness: 41.00,\n",
-       "  O...|OOOO|OOOO|...O|.O..|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 140.14,\n",
-       "  OOOO|...O|...O|O...|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 167.15,\n",
-       "  .O..|...O|...O|O...|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 95.21,\n",
-       "  ...O|.O..|.O..|OOOO|OOOO|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 222.04,\n",
-       "  OOOO|OOOO|..O.|...O|O...|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 185.67,\n",
-       "  O...|..O.|.O..|.O..|...O|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 54.67,\n",
-       "  ...O|O...|..O.|..O.|...O|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 51.80,\n",
-       "  O...|..O.|OOOO|..O.|...O|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 93.69,\n",
-       "  O...|..O.|.O..|..O.|OOOO|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 103.38,\n",
-       "  O...|OOOO|..O.|O...|.O..|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 132.06,\n",
-       "  OOOO|O...|...O|O...|OOOO|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 177.08,\n",
-       "  .O..|O...|..O.|OOOO|OOOO|.O..|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 95.92,\n",
-       "  OOOO|..O.|.O..|...O|...O|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 114.60,\n",
-       "  ..O.|...O|OOOO|...O|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 101.23,\n",
-       "  ..O.|...O|.O..|...O|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 94.25,\n",
-       "  ...O|...O|OOOO|..O.|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 292.69,\n",
-       "  ...O|...O|OOOO|OOOO|...O|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 3 fitness: 290.73,\n",
-       "  ...O|...O|.O..|OOOO|...O|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 172.15,\n",
-       "  ...O|...O|.O..|..O.|...O|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 99.81,\n",
-       "  ...O|..O.|..O.|OOOO|OOOO|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 96.97,\n",
-       "  O...|O...|..O.|...O|..O.|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 79.94,\n",
-       "  .O..|...O|O...|O...|...O|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 73.26,\n",
-       "  ...O|...O|OOOO|..O.|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 171.82,\n",
-       "  ...O|OOOO|OOOO|OOOO|O...|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 4 fitness: 530.99,\n",
-       "  ..O.|...O|O...|OOOO|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 263.92,\n",
-       "  OOOO|...O|O...|.O..|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 340.96,\n",
-       "  ..O.|...O|OOOO|OOOO|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 9 fitness: 512.44,\n",
-       "  ..O.|...O|O...|.O..|...O|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 63.83,\n",
-       "  ..O.|...O|.O..|O...|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 95.45,\n",
-       "  O...|.O..|.O..|...O|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 103.25,\n",
-       "  O...|OOOO|...O|...O|OOOO|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 13 fitness: 40.17,\n",
-       "  O...|.O..|OOOO|...O|.O..|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 145.85,\n",
-       "  OOOO|...O|...O|.O..|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 243.82,\n",
-       "  O...|O...|O...|...O|..O.|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 70.20,\n",
-       "  O...|OOOO|O...|OOOO|..O.|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 211.06,\n",
-       "  O...|.O..|.O..|OOOO|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 317.81,\n",
-       "  O...|.O..|OOOO|OOOO|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 175.09,\n",
-       "  ..O.|..O.|O...|OOOO|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 347.36,\n",
-       "  OOOO|...O|.O..|.O..|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 336.67,\n",
-       "  .O..|...O|O...|..O.|...O|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 58.83,\n",
-       "  ...O|O...|OOOO|OOOO|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 291.27,\n",
-       "  ..O.|.O..|OOOO|OOOO|.O..|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 357.58,\n",
-       "  ..O.|.O..|OOOO|.O..|.O..|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 155.43,\n",
-       "  .O..|OOOO|..O.|OOOO|O...|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 213.72,\n",
-       "  O...|.O..|.O..|OOOO|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 307.23,\n",
-       "  OOOO|.O..|OOOO|..O.|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 214.78,\n",
-       "  ...O|O...|OOOO|O...|.O..|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 303.58,\n",
-       "  O...|.O..|.O..|...O|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 297.94,\n",
-       "  O...|OOOO|OOOO|...O|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 107.90,\n",
-       "  .O..|.O..|.O..|OOOO|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 268.53,\n",
-       "  ..O.|O...|OOOO|OOOO|.O..|..OO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 334.68,\n",
-       "  ..O.|O...|OOOO|O...|.O..|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 312.97,\n",
-       "  ..O.|O...|...O|OOOO|.O..|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 260.63,\n",
-       "  ..O.|O...|OOOO|O...|.O..|..OO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 142.71,\n",
-       "  ..O.|O...|...O|O...|.O..|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 95.22,\n",
-       "  ..O.|OOOO|OOOO|OOOO|...O|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 17 fitness: 32.11,\n",
-       "  O...|O...|O...|...O|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 72.19,\n",
-       "  .O..|.O..|..O.|OOOO|OOOO|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 86.63,\n",
-       "  .O..|..O.|OOOO|O...|OOOO|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 322.23,\n",
-       "  OOOO|..O.|.O..|O...|...O|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 115.40,\n",
-       "  .O..|..O.|.O..|O...|...O|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 45.44,\n",
-       "  ...O|O...|OOOO|OOOO|..O.|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 16 fitness: 39.08,\n",
-       "  O...|.O..|..O.|O...|OOOO|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 82.02,\n",
-       "  ...O|.O..|.O..|...O|OOOO|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 116.54,\n",
-       "  ...O|.O..|OOOO|OOOO|.O..|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 231.96,\n",
-       "  OOOO|..O.|OOOO|O...|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 213.86,\n",
-       "  O...|..O.|OOOO|O...|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 204.99,\n",
-       "  OOOO|..O.|...O|O...|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 139.08,\n",
-       "  ...O|.O..|OOOO|OOOO|..O.|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 12 fitness: 50.87,\n",
-       "  ...O|.O..|OOOO|OOOO|..O.|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 128.30,\n",
-       "  OOOO|O...|OOOO|..O.|O...|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 324.13,\n",
-       "  ...O|O...|...O|..O.|O...|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 85.07,\n",
-       "  ..O.|O...|O...|...O|.O..|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 99.98,\n",
-       "  .O..|...O|...O|O...|.O..|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 123.66,\n",
-       "  ...O|...O|OOOO|OOOO|...O|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 323.39,\n",
-       "  ...O|...O|OOOO|...O|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 3 fitness: 324.20,\n",
-       "  OOOO|..O.|OOOO|.O..|.O..|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 153.10,\n",
-       "  ...O|...O|..O.|...O|...O|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 101.12,\n",
-       "  O...|O...|O...|O...|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 80.88,\n",
-       "  O...|OOOO|.O..|O...|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 281.79,\n",
-       "  .OOO|.OOO|O...|..O.|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 268.17,\n",
-       "  .O..|.O..|O...|..O.|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 98.24,\n",
-       "  OOOO|O...|OOOO|O...|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 193.76,\n",
-       "  ..O.|.O..|OOOO|O...|.O..|..OO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 200.57,\n",
-       "  O...|.O..|O...|...O|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 68.23,\n",
-       "  .O..|..O.|...O|.O..|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 83.45,\n",
-       "  .O..|..O.|OOOO|.O..|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 274.13,\n",
-       "  .O..|..O.|...O|.O..|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 55.80,\n",
-       "  .O..|O...|.O..|..O.|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 361.23,\n",
-       "  OOOO|..O.|O...|OOOO|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 223.24,\n",
-       "  ..O.|OOOO|O...|...O|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 131.27,\n",
-       "  OOOO|..O.|.O..|.O..|...O|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 119.69,\n",
-       "  ..O.|.O..|...O|O...|O...|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 96.66,\n",
-       "  ...O|..O.|O...|O...|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 77.17,\n",
-       "  ..O.|OOOO|OOOO|..O.|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 284.44,\n",
-       "  ...O|...O|..O.|OOOO|OOOO|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 85.75,\n",
-       "  OOOO|..O.|.O..|...O|OOOO|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 143.62,\n",
-       "  OOOO|.O..|..O.|.O..|OOOO|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 191.18,\n",
-       "  ...O|.O..|OOOO|OOOO|..O.|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 87.67,\n",
-       "  ...O|OOOO|.O..|...O|..O.|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 95.52,\n",
-       "  .O..|O...|.O..|OOOO|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 350.65,\n",
-       "  ..O.|OOOO|OOOO|OOOO|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 3 fitness: 525.29,\n",
-       "  ..O.|OOOO|...O|OOOO|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 299.82,\n",
-       "  O...|OOOO|O...|O...|..O.|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 291.96,\n",
-       "  OOOO|...O|OOOO|O...|..O.|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 183.75,\n",
-       "  O...|...O|O...|O...|..O.|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 56.28,\n",
-       "  ..O.|OOOO|OOOO|OOOO|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 501.80,\n",
-       "  ..O.|..O.|..O.|OOOO|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 340.29,\n",
-       "  ..O.|..O.|..O.|OOOO|..OO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 231.72,\n",
-       "  .O..|OOOO|..O.|.O..|OOOO|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 195.60,\n",
-       "  OOOO|.O..|..O.|OOOO|..O.|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 108.18,\n",
-       "  .O..|.O..|...O|OOOO|OOOO|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 3 fitness: 153.17,\n",
-       "  OOOO|O...|O...|...O|..O.|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 154.18,\n",
-       "  .O..|..O.|OOOO|O...|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 8 fitness: 536.14,\n",
-       "  O...|..O.|.O..|...O|..O.|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 69.76,\n",
-       "  ...O|O...|OOOO|OOOO|O...|OOO.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 417.84,\n",
-       "  ...O|OOOO|...O|O...|OOOO|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 168.65,\n",
-       "  ..O.|OOOO|OOOO|...O|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 330.81,\n",
-       "  ..O.|OOOO|..O.|...O|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 3 fitness: 217.70,\n",
-       "  ...O|..O.|...O|OOOO|OOOO|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 113.46,\n",
-       "  ...O|OOOO|.O..|.O..|OOOO|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 137.08,\n",
-       "  ...O|O...|.O..|.O..|OOOO|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 102.56,\n",
-       "  O...|.O..|O...|OOOO|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 318.66,\n",
-       "  O...|O...|OOOO|OOOO|O...|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 270.60,\n",
-       "  ..O.|OOOO|...O|...O|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 197.62,\n",
-       "  ..O.|...O|OOOO|...O|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 241.23,\n",
-       "  ..O.|OOOO|...O|OOOO|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 270.02,\n",
-       "  .O..|O...|.O..|OOOO|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 11 fitness: 535.42,\n",
-       "  .O..|O...|.O..|OOOO|..O.|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 290.73,\n",
-       "  O...|O...|..O.|OOOO|O...|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 100.23,\n",
-       "  OOOO|..O.|.O..|...O|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 144.85,\n",
-       "  ..O.|...O|...O|..O.|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 100.31,\n",
-       "  ..O.|...O|OOOO|..O.|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 324.18,\n",
-       "  ..O.|...O|OOOO|..O.|...O|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 163.83,\n",
-       "  ..O.|...O|...O|..O.|...O|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 43.70,\n",
-       "  ..O.|...O|OOOO|OOOO|...O|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 214.44,\n",
-       "  ..O.|OOOO|...O|..O.|...O|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 111.06,\n",
-       "  ..O.|OOOO|OOOO|..O.|...O|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 77.42,\n",
-       "  OOOO|..O.|..O.|O...|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 376.68,\n",
-       "  OOOO|..O.|OOOO|O...|...O|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 283.59,\n",
-       "  OOOO|.O..|O...|.O..|.O..|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 288.76,\n",
-       "  ..O.|...O|OOOO|...O|OOOO|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 3 fitness: 80.88,\n",
-       "  .O..|OOOO|..O.|OOOO|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 156.72,\n",
-       "  ..O.|O...|..O.|.O..|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 64.40,\n",
-       "  ..O.|O...|OOOO|OOOO|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 6 fitness: 508.70,\n",
-       "  OOOO|..O.|...O|O...|OOOO|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 100.35,\n",
-       "  .O..|..O.|OOOO|OOOO|O...|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 195.18,\n",
-       "  OOOO|..OO|..O.|O...|...O|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 97.34,\n",
-       "  .O..|O...|.O..|OOOO|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 338.86,\n",
-       "  OOOO|.O..|.O..|OOOO|...O|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 127.22,\n",
-       "  ..O.|O...|...O|.O..|O...|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 93.90,\n",
-       "  ..O.|.O..|OOOO|OOOO|..O.|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 15 fitness: 38.76,\n",
-       "  ..O.|OOOO|OOOO|...O|.O..|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 119.30,\n",
-       "  OOOO|..O.|..O.|..O.|O...|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 114.89,\n",
-       "  ...O|...O|..O.|OOOO|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 311.44,\n",
-       "  ...O|...O|..O.|..O.|.OOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 92.06,\n",
-       "  OOOO|OOOO|...O|.O..|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 262.93,\n",
-       "  ..O.|O...|.O..|..O.|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 167.09,\n",
-       "  ...O|...O|..O.|..O.|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 149.37,\n",
-       "  .O..|OOOO|...O|.O..|OOOO|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 131.55,\n",
-       "  OOOO|O...|O...|O...|.O..|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 306.96,\n",
-       "  OOOO|O...|OOOO|.O..|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 220.24,\n",
-       "  .O..|OOOO|.O..|OOOO|..OO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 319.47,\n",
-       "  OOOO|..O.|...O|O...|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 259.78,\n",
-       "  .O..|...O|...O|O...|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 81.34,\n",
-       "  ...O|OOOO|.O..|.O..|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 224.13,\n",
-       "  ...O|..O.|OOOO|OOOO|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 286.94,\n",
-       "  .O..|..O.|O...|..O.|OOOO|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 116.71,\n",
-       "  O...|.O..|O...|OOOO|...O|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 259.33,\n",
-       "  .O..|O...|O...|O...|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 107.19,\n",
-       "  OOOO|O...|.O..|OOOO|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 150.53,\n",
-       "  .O..|O...|..O.|OOOO|OOOO|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 108.96,\n",
-       "  OOOO|O...|..O.|OOOO|...O|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 81.01,\n",
-       "  .O..|O...|..O.|OOOO|...O|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 46.14,\n",
-       "  ..O.|...O|...O|..O.|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 91.16,\n",
-       "  ..O.|OOOO|...O|..O.|...O|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 106.26,\n",
-       "  OOOO|...O|...O|..O.|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 164.62,\n",
-       "  OOOO|...O|OOOO|.O..|O...|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 151.47,\n",
-       "  O...|..O.|O...|...O|..O.|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 74.89,\n",
-       "  OOOO|.O..|..O.|OOOO|O...|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 177.55,\n",
-       "  ...O|..O.|OOOO|OOOO|...O|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 281.55,\n",
-       "  OOOO|O...|.O..|..O.|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 222.85,\n",
-       "  O...|.O..|OOOO|OOOO|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 290.43,\n",
-       "  OOOO|.O..|...O|.O..|..O.|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 125.72,\n",
-       "  .O..|.O..|..O.|OOOO|OOOO|.O..|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 134.99,\n",
-       "  ..O.|OOOO|..O.|OOOO|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 305.37,\n",
-       "  OOOO|..O.|...O|O...|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 199.38,\n",
-       "  .O..|O...|...O|OOOO|OOOO|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 4 fitness: 127.90,\n",
-       "  ..O.|OOOO|.O..|OOOO|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 142.88,\n",
-       "  O...|..O.|...O|.O..|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 4 fitness: 145.22,\n",
-       "  ..O.|..O.|...O|..O.|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 90.12,\n",
-       "  OOOO|..O.|...O|OOOO|...O|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 155.73,\n",
-       "  O...|...O|.O..|OOOO|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 229.86,\n",
-       "  ..O.|.O..|OOOO|OOOO|..O.|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 96.37,\n",
-       "  ..O.|...O|..O.|OOOO|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 331.22,\n",
-       "  ..O.|...O|..O.|.O..|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 57.60,\n",
-       "  .O..|OOOO|..O.|OOOO|O...|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 178.74,\n",
-       "  .O..|.O..|...O|..O.|...O|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 93.93,\n",
-       "  OOOO|OOOO|...O|..O.|...O|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 3 fitness: 68.96,\n",
-       "  ..O.|O...|...O|.O..|.O..|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 61.57,\n",
-       "  OOOO|O...|O...|O...|..O.|.O..|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 125.29,\n",
-       "  OOOO|O...|...O|OOOO|..O.|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 135.15,\n",
-       "  ..O.|.O..|.O..|.O..|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 89.99,\n",
-       "  ..O.|.O..|OOOO|OOOO|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 260.24,\n",
-       "  O...|.O..|OOOO|.O..|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 121.76,\n",
-       "  O...|OOOO|...O|.O..|..O.|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 146.05,\n",
-       "  ...O|O...|OOOO|OOOO|..O.|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 123.28,\n",
-       "  O...|O...|..O.|.O..|O...|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 134.19,\n",
-       "  O...|O...|..O.|OOOO|O...|.O..|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 89.05,\n",
-       "  O...|OOOO|OOOO|O...|...O|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 280.13,\n",
-       "  O...|...O|..O.|O...|...O|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 40.39,\n",
-       "  OOOO|O...|..O.|O...|.O..|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 152.58,\n",
-       "  OOOO|.O..|.O..|OOOO|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 326.93,\n",
-       "  ..O.|...O|OOOO|O...|OOOO|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 3 fitness: 103.71,\n",
-       "  OOOO|...O|OOOO|..O.|...O|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 165.68,\n",
-       "  ...O|...O|...O|..O.|...O|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 50.14,\n",
-       "  ...O|OOOO|..O.|.O..|O...|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 171.92,\n",
-       "  OOOO|O...|OOOO|.O..|O...|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 168.80,\n",
-       "  ...O|O...|..O.|OOOO|O...|OOO.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 257.69,\n",
-       "  OOOO|...O|O...|.O..|...O|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 144.50,\n",
-       "  .O..|OOOO|.O..|O...|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 321.81,\n",
-       "  ..O.|...O|OOOO|OOOO|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 295.03,\n",
-       "  ...O|O...|OOOO|OOOO|O...|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 325.57,\n",
-       "  ...O|OOOO|...O|...O|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 311.36,\n",
-       "  O...|O...|O...|OOOO|...O|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 311.61,\n",
-       "  O...|O...|O...|OOOO|...O|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 134.80,\n",
-       "  O...|O...|O...|..O.|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 252.35,\n",
-       "  O...|O...|O...|..O.|...O|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 55.41,\n",
-       "  .O..|..O.|OOOO|O...|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 3 fitness: 301.88,\n",
-       "  .O..|..O.|...O|O...|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 160.52,\n",
-       "  O...|...O|OOOO|...O|..O.|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 82.82,\n",
-       "  O...|...O|..O.|.O..|OOOO|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 134.76,\n",
-       "  O...|O...|O...|...O|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 42.64,\n",
-       "  ..O.|OOOO|OOOO|.O..|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 143.70,\n",
-       "  ..O.|...O|OOOO|.O..|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 201.46,\n",
-       "  ..O.|..O.|OOOO|..O.|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 274.76,\n",
-       "  .OOO|.OOO|...O|..O.|...O|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 156.80,\n",
-       "  ..O.|..O.|OOOO|..O.|...O|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 90.16,\n",
-       "  ..O.|O...|.O..|..O.|...O|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 95.84,\n",
-       "  .O..|..O.|O...|O...|.O..|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 152.30,\n",
-       "  .O..|..O.|OOOO|O...|.O..|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 111.47,\n",
-       "  .O..|OOOO|..O.|..O.|OOOO|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 105.21,\n",
-       "  OOOO|O...|OOOO|OOOO|...O|.O..|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 6 fitness: 247.33,\n",
-       "  ...O|...O|OOOO|OOOO|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 14 fitness: 489.32,\n",
-       "  ...O|OOOO|...O|OOOO|...O|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 136.18,\n",
-       "  ...O|OOOO|..O.|OOOO|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 286.88,\n",
-       "  OOOO|..O.|O...|..O.|..O.|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 171.77,\n",
-       "  .O..|OOOO|OOOO|..O.|..O.|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 162.54,\n",
-       "  O...|.O..|O...|O...|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 79.01,\n",
-       "  O...|.O..|O...|OOOO|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 145.42,\n",
-       "  O...|.O..|O...|OOOO|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 200.20,\n",
-       "  .O..|..O.|O...|OOOO|..O.|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 135.61,\n",
-       "  .O..|..O.|O...|O...|..O.|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 66.75,\n",
-       "  ...O|O...|...O|OOOO|.O..|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 247.61,\n",
-       "  .O..|..O.|O...|.O..|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 258.97,\n",
-       "  ...O|OOOO|..O.|.O..|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 167.69,\n",
-       "  OOOO|.O..|OOOO|O...|..O.|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 148.39,\n",
-       "  OOOO|OOOO|..O.|.O..|.O..|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 181.40,\n",
-       "  ..O.|OOOO|..O.|.O..|.O..|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 53.58,\n",
-       "  ..O.|.O..|OOOO|O...|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 241.72,\n",
-       "  OOOO|..O.|OOOO|..O.|..O.|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 130.09,\n",
-       "  ..O.|OOOO|...O|O...|.O..|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 118.74,\n",
-       "  ...O|OOOO|...O|.O..|O...|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 147.39,\n",
-       "  OOOO|.O..|.O..|OOOO|..O.|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 239.28,\n",
-       "  ...O|.O..|.O..|OOOO|..O.|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 53.31,\n",
-       "  O...|OOOO|O...|O...|OOOO|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 330.54,\n",
-       "  ..O.|..O.|..O.|OOOO|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 291.43,\n",
-       "  .OOO|.OOO|..O.|..O.|...O|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 211.78,\n",
-       "  O...|.O..|..O.|OOOO|O...|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 119.07,\n",
-       "  O...|OOOO|..O.|...O|O...|.O..|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 82.96,\n",
-       "  .O..|..O.|.O..|...O|OOOO|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 100.72,\n",
-       "  ..O.|OOOO|OOOO|.O..|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 177.91,\n",
-       "  ..O.|O...|OOOO|OOOO|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 289.57,\n",
-       "  ..O.|O...|...O|OOOO|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 62.25,\n",
-       "  ...O|O...|...O|..O.|...O|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 84.66,\n",
-       "  OOOO|OOOO|.O..|.O..|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 320.71,\n",
-       "  ..O.|O...|OOOO|OOOO|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 194.05,\n",
-       "  O...|OOOO|...O|OOOO|..O.|.O..|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 195.82,\n",
-       "  OOOO|O...|O...|...O|...O|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 188.91,\n",
-       "  .O..|O...|O...|...O|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 201.56,\n",
-       "  .O..|OOOO|..O.|OOOO|O...|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 78.59,\n",
-       "  ...O|OOOO|...O|.O..|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 212.76,\n",
-       "  OOOO|.O..|..O.|...O|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 188.44,\n",
-       "  OOOO|OOOO|.O..|...O|...O|.O..|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 161.73,\n",
-       "  O...|.O..|OOOO|..O.|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 124.21,\n",
-       "  O...|OOOO|.O..|O...|OOOO|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 334.97,\n",
-       "  ...O|OOOO|..O.|OOOO|...O|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 76.81,\n",
-       "  OOOO|OOOO|O...|.O..|...O|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 196.38,\n",
-       "  ...O|OOOO|OOOO|.O..|...O|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 139.13,\n",
-       "  OOOO|.O..|OOOO|..O.|O...|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 227.71,\n",
-       "  OOOO|...O|...O|.O..|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 306.12,\n",
-       "  OOOO|OOOO|...O|.O..|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 159.71,\n",
-       "  O...|.O..|OOOO|OOOO|...O|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 129.45,\n",
-       "  .O..|OOOO|..O.|.O..|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 125.74,\n",
-       "  .O..|OOOO|..O.|.O..|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 90.41,\n",
-       "  .O..|...O|..OO|OO..|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 96.44,\n",
-       "  ...O|.O..|OOOO|.O..|...O|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 3 fitness: 126.35,\n",
-       "  ..O.|.O..|..O.|O...|.O..|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 60.19,\n",
-       "  O...|O...|O...|...O|...O|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 49.52,\n",
-       "  O...|O...|O...|...O|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 108.92,\n",
-       "  O...|O...|.O..|..O.|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 196.80,\n",
-       "  .OOO|.OOO|O...|..O.|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 236.93,\n",
-       "  .O..|...O|OOOO|O...|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 263.82,\n",
-       "  ..O.|OOOO|OOOO|OOOO|...O|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 6 fitness: 110.42,\n",
-       "  O...|...O|..O.|O...|...O|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 130.61,\n",
-       "  OOOO|...O|..O.|..O.|...O|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 160.66,\n",
-       "  O...|OOOO|OOOO|..O.|OOOO|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 177.38,\n",
-       "  ..O.|...O|...O|..O.|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 99.72,\n",
-       "  OOOO|...O|...O|OOOO|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 243.98,\n",
-       "  O...|OOOO|OOOO|O...|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 330.40,\n",
-       "  ...O|OOOO|..O.|...O|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 334.83,\n",
-       "  ...O|OOOO|..O.|...O|O...|OOO.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 213.51,\n",
-       "  O...|O...|OOOO|O...|...O|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 155.32,\n",
-       "  .OOO|...O|..O.|..O.|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 69.91,\n",
-       "  .O..|.O..|..O.|O...|OOOO|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 125.27,\n",
-       "  .O..|.O..|..O.|OOOO|OOOO|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 121.14,\n",
-       "  .O..|.O..|..O.|OOOO|.O..|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 106.14,\n",
-       "  ..O.|OOOO|..O.|OOOO|...O|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 79.21,\n",
-       "  .O..|O...|..O.|...O|OOOO|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 55.43,\n",
-       "  OOOO|O...|OOOO|O...|...O|.O..|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 192.59,\n",
-       "  OOOO|OOOO|.O..|O...|...O|.O..|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 190.42,\n",
-       "  OOOO|.O..|O...|O...|...O|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 103.84,\n",
-       "  O...|OOOO|..O.|...O|OOOO|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 140.51,\n",
-       "  ..O.|...O|OOOO|..O.|...O|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 129.77,\n",
-       "  ..O.|...O|...O|...O|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 91.03,\n",
-       "  .OOO|.OOO|O...|..O.|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 171.02,\n",
-       "  OOOO|..O.|O...|..O.|..O.|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 132.59,\n",
-       "  ..OO|..O.|OOOO|..O.|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 5 fitness: 334.01,\n",
-       "  ..O.|..O.|OOOO|..O.|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 148.39,\n",
-       "  .O..|OOOO|.O..|.O..|OOOO|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 339.15,\n",
-       "  OOOO|...O|...O|..O.|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 197.11,\n",
-       "  ...O|OOOO|OOOO|..O.|..O.|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 140.37,\n",
-       "  OOOO|.O..|...O|OOOO|..O.|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 27.44,\n",
-       "  O...|.O..|.O..|OOOO|...O|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 132.90,\n",
-       "  O...|OOOO|.O..|OOOO|...O|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 131.84,\n",
-       "  ..O.|...O|OOOO|O...|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 101.38,\n",
-       "  O...|..O.|OOOO|...O|.O..|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 84.87,\n",
-       "  O...|...O|...O|.O..|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 293.03,\n",
-       "  .O..|.O..|..O.|..O.|.O..|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 110.22,\n",
-       "  .O..|OOOO|..O.|..OO|OOOO|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 51.49,\n",
-       "  .O..|O...|.O..|OOOO|OOOO|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 333.10,\n",
-       "  OOOO|O...|..O.|..O.|...O|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 120.88,\n",
-       "  ...O|OOOO|..O.|..O.|...O|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 32.24,\n",
-       "  O...|.O..|O...|...O|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 218.32,\n",
-       "  O...|.O..|O...|...O|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 58.67,\n",
-       "  OOOO|OOOO|O...|...O|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 147.49,\n",
-       "  O...|OOOO|O...|...O|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 64.98,\n",
-       "  OOOO|.O..|O...|...O|..O.|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 102.22,\n",
-       "  ..O.|...O|OOOO|...O|..O.|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 19.98,\n",
-       "  OOOO|OOOO|.O..|O...|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 332.50,\n",
-       "  .O..|O...|...O|..O.|.O..|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 82.89,\n",
-       "  OOOO|OOOO|...O|.O..|..O.|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 173.12,\n",
-       "  OOOO|..O.|OOOO|...O|OOOO|..OO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 17 fitness: 7.93,\n",
-       "  ...O|.O..|..O.|.O..|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 165.18,\n",
-       "  ...O|.O..|..O.|OOOO|O...|OOO.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 241.57,\n",
-       "  .O..|O...|O...|OOOO|.O..|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 130.17,\n",
-       "  ...O|.O..|.O..|.O..|O...|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 114.79,\n",
-       "  ...O|OOOO|.O..|.O..|O...|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 133.17,\n",
-       "  OOOO|.O..|.O..|OOOO|O...|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 286.30,\n",
-       "  ...O|.O..|.O..|OOOO|O...|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 231.68,\n",
-       "  ...O|.O..|OOOO|.O..|O...|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 180.05,\n",
-       "  O...|OOOO|.O..|.O..|O...|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 154.22,\n",
-       "  ...O|...O|...O|OOOO|...O|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 165.27,\n",
-       "  ...O|O...|.O..|..O.|...O|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 71.07,\n",
-       "  OOOO|OOOO|...O|..O.|O...|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 126.86,\n",
-       "  OOOO|O...|OOOO|O...|...O|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 161.82,\n",
-       "  ...O|O...|.O..|OOOO|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 173.22,\n",
-       "  OOOO|O...|.O..|OOOO|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 310.00,\n",
-       "  ...O|O...|.O..|.O..|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 138.54,\n",
-       "  ...O|O...|OOOO|.O..|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 160.14,\n",
-       "  ..O.|..O.|...O|..O.|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 104.51,\n",
-       "  ...O|OOOO|OOOO|OOOO|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 170.99,\n",
-       "  O...|...O|OOOO|...O|.O..|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 90.02,\n",
-       "  OOOO|O...|O...|..O.|..O.|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 193.79,\n",
-       "  ...O|O...|O...|..O.|..O.|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 108.63,\n",
-       "  ...O|..O.|OOOO|..OO|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 104.72,\n",
-       "  .O..|.O..|.O..|.O..|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 80.30,\n",
-       "  OOOO|...O|..O.|.OOO|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 147.42,\n",
-       "  ...O|O...|OOOO|O...|...O|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 129.92,\n",
-       "  ...O|O...|OOOO|O...|...O|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 111.29,\n",
-       "  .O..|O...|O...|.O..|..O.|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 88.25,\n",
-       "  .O..|O...|OOOO|...O|.O..|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 206.02,\n",
-       "  OOOO|O...|O...|...O|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 223.14,\n",
-       "  ...O|O...|O...|...O|.O..|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 126.43,\n",
-       "  OOOO|O...|O...|...O|OOOO|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 228.58,\n",
-       "  ...O|..O.|O...|OOOO|..OO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 191.13,\n",
-       "  O...|...O|OOOO|.O..|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 290.60,\n",
-       "  O...|...O|..O.|.O..|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 144.95,\n",
-       "  .O..|OOOO|...O|..O.|..O.|.O..|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 130.12,\n",
-       "  ...O|..O.|OOOO|...O|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 256.35,\n",
-       "  ...O|...O|OOOO|O...|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 167.58,\n",
-       "  OOOO|OOOO|O...|..O.|...O|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 80.01,\n",
-       "  ...O|..O.|..O.|.O..|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 195.14,\n",
-       "  ...O|..O.|OOOO|.O..|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 253.60,\n",
-       "  .O..|O...|.O..|...O|...O|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 54.58,\n",
-       "  .O..|O...|.O..|...O|OOOO|OO..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 178.11,\n",
-       "  ..O.|O...|OOOO|OOOO|...O|OO..|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 103.39,\n",
-       "  ...O|OOOO|..O.|.O..|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 147.10,\n",
-       "  .O..|O...|.O..|...O|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 160.91,\n",
-       "  ...O|O...|OOOO|OOOO|...O|OO..|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 2 fitness: 77.12,\n",
-       "  ...O|...O|O...|OOOO|...O|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 118.55,\n",
-       "  ...O|OOOO|OOOO|OOOO|...O|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 6 fitness: 55.93,\n",
-       "  OOOO|...O|O...|OOOO|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 251.56,\n",
-       "  OOOO|OOOO|..O.|..O.|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 110.41,\n",
-       "  OOOO|.O..|..OO|O...|..O.|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 51.17,\n",
-       "  ..O.|O...|..O.|..O.|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 179.94,\n",
-       "  ..O.|...O|O...|OOOO|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 120.38,\n",
-       "  OOOO|...O|O...|..O.|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 102.66,\n",
-       "  .O..|O...|..O.|OOOO|.O..|.O..|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 103.89,\n",
-       "  .O..|O...|..O.|OOOO|.O..|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 168.66,\n",
-       "  .O..|O...|...O|..O.|OOOO|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 107.64,\n",
-       "  OOOO|O...|...O|OOOO|...O|OO..|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 5 fitness: 67.62,\n",
-       "  ...O|OOOO|..OO|.O..|...O|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 62.60,\n",
-       "  ..O.|OOOO|OOOO|O...|O...|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 139.90,\n",
-       "  .O..|OOOO|O...|O...|OOOO|..OO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 336.04,\n",
-       "  ...O|OOOO|OOOO|O...|O...|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 328.35,\n",
-       "  O...|OOOO|.O..|..O.|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 109.74,\n",
-       "  O...|O...|.O..|OOOO|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 113.50,\n",
-       "  ...O|O...|...O|OOOO|OO..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 160.39,\n",
-       "  ...O|.O..|OOOO|OOOO|..O.|.O..|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 78.68,\n",
-       "  .OOO|.OOO|O...|..O.|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 194.20,\n",
-       "  ...O|...O|O...|..O.|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 132.23,\n",
-       "  ...O|.OOO|O...|..O.|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 232.90,\n",
-       "  .O..|.O..|..O.|OOOO|O...|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 134.64,\n",
-       "  .O..|OOOO|..O.|OOOO|O...|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 80.47,\n",
-       "  OOOO|O...|O...|...O|OOOO|OO..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 243.68,\n",
-       "  ..O.|O...|O...|.O..|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 135.68,\n",
-       "  O...|..O.|..O.|O...|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 297.28,\n",
-       "  OOOO|OOOO|.O..|...O|..O.|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 166.75,\n",
-       "  ...O|O...|OOOO|..O.|.O..|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 300.05,\n",
-       "  ...O|O...|...O|..O.|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 93.54,\n",
-       "  ...O|OOOO|...O|..O.|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 129.34,\n",
-       "  ...O|.O..|OOOO|..O.|.O..|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 275.42,\n",
-       "  ...O|OOOO|...O|..O.|.O..|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 82.97,\n",
-       "  ..O.|O...|O...|.O..|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 164.09,\n",
-       "  ...O|..O.|.O..|..O.|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 88.87,\n",
-       "  ...O|...O|...O|OOOO|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 222.96,\n",
-       "  ...O|OOOO|OOOO|OO..|...O|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 70.76,\n",
-       "  .O..|.O..|.O..|OOOO|.O..|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 107.81,\n",
-       "  ..O.|OOOO|..O.|..O.|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 85.97,\n",
-       "  OOOO|O...|..O.|..O.|.O..|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 170.54,\n",
-       "  ...O|...O|...O|.O..|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 143.42,\n",
-       "  ...O|...O|...O|OOOO|..O.|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 176.11,\n",
-       "  .O..|O...|O...|..O.|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 97.16,\n",
-       "  OOOO|.O..|OOOO|..O.|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 139.16,\n",
-       "  .OOO|O...|...O|..O.|...O|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 105.19,\n",
-       "  OOOO|OOOO|.O..|.O..|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 308.00,\n",
-       "  O...|O...|..O.|...O|..O.|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 31.01,\n",
-       "  OOOO|OOOO|.O..|.O..|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 381.18,\n",
-       "  OOOO|O...|...O|O...|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 188.80,\n",
-       "  ..O.|OOOO|OOOO|O...|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 102.59,\n",
-       "  OOOO|O...|.O..|..O.|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 161.77,\n",
-       "  ..O.|O...|..O.|OOOO|.O..|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 259.67,\n",
-       "  ..O.|..O.|...O|OOOO|OOOO|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 3 fitness: 34.98,\n",
-       "  O...|OOOO|..O.|..O.|O...|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 114.23,\n",
-       "  ..O.|OOOO|.O..|.O..|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 134.91,\n",
-       "  ..O.|OOOO|.O..|.O..|.O..|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 210.72,\n",
-       "  OOOO|O...|OOOO|.O..|...O|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 146.94,\n",
-       "  O...|OOOO|OOOO|O...|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 290.76,\n",
-       "  O...|...O|O...|OOOO|..O.|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 139.47,\n",
-       "  .O..|OOOO|...O|...O|..O.|.O..|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 121.68,\n",
-       "  .O..|OO..|...O|...O|OOOO|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 34.80,\n",
-       "  ...O|.O..|.O..|.O..|OOOO|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 202.01,\n",
-       "  OOOO|...O|...O|..O.|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 138.71,\n",
-       "  ...O|...O|...O|..O.|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 3 fitness: 125.85,\n",
-       "  .O..|.O..|OOOO|...O|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 212.64,\n",
-       "  OOOO|O...|...O|O...|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 174.51,\n",
-       "  OOOO|O...|...O|O...|.O..|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 133.14,\n",
-       "  ...O|OOOO|O...|OOOO|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 290.33,\n",
-       "  ...O|OOOO|O...|..O.|.O..|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 272.72,\n",
-       "  ..O.|...O|OOOO|...O|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 99.63,\n",
-       "  ...O|..O.|..O.|OOOO|..O.|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 149.93,\n",
-       "  .O..|...O|OOOO|O...|..O.|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 150.77,\n",
-       "  .O..|OOOO|OOOO|O...|..O.|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 127.32,\n",
-       "  O...|.O..|...O|..O.|...O|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 137.31,\n",
-       "  ..O.|..O.|OOOO|..O.|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 113.79,\n",
-       "  OOOO|.O..|.O..|OOOO|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 138.64,\n",
-       "  OOOO|.O..|OOOO|O...|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 123.07,\n",
-       "  OOOO|O...|O...|OOOO|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 278.63,\n",
-       "  ...O|OOOO|..O.|...O|O...|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 115.62,\n",
-       "  ..O.|O...|.O..|O...|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 216.90,\n",
-       "  O...|OOOO|.O..|...O|..O.|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 105.43,\n",
-       "  OOOO|...O|O...|...O|...O|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 179.82,\n",
-       "  .O..|.O..|.O..|..O.|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 123.96,\n",
-       "  ..O.|.O..|...O|O...|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 214.56,\n",
-       "  ..O.|.O..|OOOO|O...|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 285.58,\n",
-       "  ...O|OOOO|...O|..O.|...O|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 84.20,\n",
-       "  O...|.O..|.O..|OOOO|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 325.29,\n",
-       "  O...|..O.|OOOO|...O|...O|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 106.17,\n",
-       "  ...O|.O..|OOOO|OOOO|...O|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 115.19,\n",
-       "  OOOO|..O.|..O.|O...|.O..|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 55.58,\n",
-       "  ..O.|..O.|OOOO|OOOO|...O|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 3 fitness: 301.41,\n",
-       "  O...|.O..|OOOO|..O.|OOOO|.O..|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 140.10,\n",
-       "  .O..|.O..|OOOO|O...|...O|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 133.73,\n",
-       "  OOOO|.O..|O...|OOOO|...O|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 115.09,\n",
-       "  .O..|...O|O...|..O.|..O.|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 135.69,\n",
-       "  O...|O...|..O.|OOOO|.O..|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 171.64,\n",
-       "  ...O|.O..|OOOO|..O.|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 154.17,\n",
-       "  ...O|.O..|...O|..O.|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 117.61,\n",
-       "  O...|.O..|OOOO|..O.|...O|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 108.25,\n",
-       "  OOOO|.O..|O...|..O.|...O|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 60.70,\n",
-       "  OOOO|O...|O...|O...|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 117.20,\n",
-       "  .O..|OOOO|O...|OOOO|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 220.94,\n",
-       "  .O..|...O|.O..|O...|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 184.70,\n",
-       "  O...|.O..|..O.|OOOO|O...|.O..|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 85.26,\n",
-       "  .O..|O...|...O|..O.|.O..|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 133.17,\n",
-       "  ..O.|.O..|OOOO|...O|..O.|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 45.77,\n",
-       "  ...O|OO..|...O|OOOO|...O|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 28.05,\n",
-       "  OOOO|O...|...O|OOOO|OO..|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 212.01,\n",
-       "  O...|.O..|...O|OOOO|..O.|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 173.74,\n",
-       "  ...O|OOOO|...O|.O..|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 139.72,\n",
-       "  OOOO|...O|...O|..O.|.O..|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 79.32,\n",
-       "  OOOO|...O|...O|.O..|.O..|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 192.82,\n",
-       "  OOOO|.O..|O...|..O.|.O..|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 191.93,\n",
-       "  ...O|..O.|.O..|.O..|...O|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 64.97,\n",
-       "  OOOO|.O..|..O.|O...|O...|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 121.78,\n",
-       "  ..O.|O...|..O.|OOOO|.O..|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 96.03,\n",
-       "  ..O.|O...|..O.|...O|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 140.74,\n",
-       "  ..O.|OOOO|...O|...O|O...|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 181.90,\n",
-       "  O...|.O..|OOOO|...O|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 91.85,\n",
-       "  O...|..O.|..O.|..O.|O...|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 112.52,\n",
-       "  O...|..O.|..O.|..O.|O...|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 132.66,\n",
-       "  ..O.|O...|..O.|...O|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 205.56,\n",
-       "  ...O|O...|..O.|..O.|OOOO|.O..|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 131.07,\n",
-       "  OOOO|.O..|O...|..O.|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 176.03,\n",
-       "  O...|OOOO|O...|..O.|OOOO|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 172.79,\n",
-       "  ...O|.O..|OOOO|..O.|..O.|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 125.75,\n",
-       "  ...O|.O..|..O.|..O.|..O.|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 128.21,\n",
-       "  ..O.|OOOO|O...|.O..|...O|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 169.83,\n",
-       "  ..O.|...O|O...|..O.|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 112.38,\n",
-       "  ...O|OOOO|OOOO|O...|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 254.56,\n",
-       "  ...O|OOOO|.O..|O...|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 175.05,\n",
-       "  O...|OOOO|O...|OOOO|..O.|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 300.51,\n",
-       "  O...|O...|O...|OOOO|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 394.63,\n",
-       "  .O..|.O..|.O..|...O|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 130.27,\n",
-       "  O...|OOOO|...O|..O.|OOOO|.O..|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 126.96,\n",
-       "  ...O|O...|O...|O...|.O..|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 127.31,\n",
-       "  .O..|OOO.|OOOO|...O|..O.|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 155.40,\n",
-       "  O...|OOOO|O...|..O.|OOOO|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 114.23,\n",
-       "  OOOO|OOOO|O...|..O.|...O|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 194.65,\n",
-       "  ...O|OOOO|...O|O...|OOOO|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 117.49,\n",
-       "  ...O|O...|...O|OOOO|OOOO|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 121.38,\n",
-       "  ...O|OOOO|..O.|.O..|OOOO|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 185.08,\n",
-       "  O...|.O..|.O..|...O|OOOO|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 187.80,\n",
-       "  OOOO|.O..|.O..|OOOO|..O.|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 194.42,\n",
-       "  OOOO|.O..|OOOO|...O|..O.|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 122.66,\n",
-       "  OOOO|OOOO|.O..|...O|..O.|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 138.10,\n",
-       "  O...|OOOO|OOOO|...O|..O.|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 147.14,\n",
-       "  OOOO|..O.|...O|O...|O...|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 157.80,\n",
-       "  OOOO|.O..|.O..|..O.|..O.|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 96.38,\n",
-       "  ..O.|OOOO|.O..|O...|O...|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 119.18,\n",
-       "  ..O.|...O|..O.|O...|..O.|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 98.71,\n",
-       "  OOOO|O...|..O.|..O.|OOOO|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 97.84,\n",
-       "  ..O.|O...|..O.|..O.|.O..|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 96.48,\n",
-       "  O...|O...|..O.|OOOO|OOOO|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 152.97,\n",
-       "  .O..|.O..|OOOO|OOOO|...O|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 161.87,\n",
-       "  ..O.|OOOO|OOOO|O...|.O..|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 110.83,\n",
-       "  OOOO|.O..|O...|..O.|.O..|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 129.48,\n",
-       "  .O..|OOOO|..O.|O...|O...|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 133.63,\n",
-       "  OOOO|..O.|OOOO|O...|O...|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 186.49,\n",
-       "  OOOO|..O.|..O.|O...|OOOO|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 139.24,\n",
-       "  OOOO|...O|O...|..O.|...O|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 123.37,\n",
-       "  .OOO|.OOO|O...|..O.|...O|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 198.66,\n",
-       "  OOOO|...O|O...|OOOO|...O|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 210.75,\n",
-       "  .O..|...O|..O.|O...|...O|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 132.48,\n",
-       "  OOOO|...O|...O|O...|...O|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 132.39,\n",
-       "  O...|...O|OOOO|OO..|OOOO|.OOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 441.87,\n",
-       "  O...|...O|...O|O...|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 76.03,\n",
-       "  OOOO|.O..|...O|OOOO|...O|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 99.99,\n",
-       "  O...|O...|O...|...O|OOOO|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 241.72,\n",
-       "  ...O|OOOO|OOO.|OOOO|..O.|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 29.45,\n",
-       "  O...|...O|OOOO|...O|..O.|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 99.68,\n",
-       "  O...|OOOO|..O.|..O.|.O..|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 115.18,\n",
-       "  O...|O...|OOOO|..O.|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 46.81,\n",
-       "  OOOO|.O..|O...|...O|.O..|..OO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 163.27,\n",
-       "  .O..|O...|OOOO|..O.|.O..|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 129.12,\n",
-       "  .O..|O...|.O..|..O.|OOOO|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 167.19,\n",
-       "  OOOO|OOOO|..O.|.O..|...O|.O..|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 193.13,\n",
-       "  OOOO|OOOO|...O|.O..|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 159.05,\n",
-       "  ..O.|...O|OOOO|O...|O...|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 107.57,\n",
-       "  OOOO|...O|O...|OOOO|...O|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 131.58,\n",
-       "  ..O.|.O..|OOOO|..O.|O...|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 175.63,\n",
-       "  OOOO|..O.|O...|..O.|..O.|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 126.66,\n",
-       "  O...|..O.|OOOO|..O.|OOOO|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 161.01,\n",
-       "  O...|..O.|O...|..O.|OOOO|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 56.44,\n",
-       "  OOOO|..O.|.O..|...O|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 81.96,\n",
-       "  ..O.|..O.|OOOO|OOOO|.O..|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 252.86,\n",
-       "  O...|.O..|...O|...O|.O..|OOOO|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 108.81,\n",
-       "  O...|...O|OOOO|..O.|OOOO|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 146.13,\n",
-       "  OOOO|OOOO|...O|.O..|O...|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 164.25,\n",
-       "  ..O.|OOOO|..O.|..O.|.O..|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 57.70,\n",
-       "  .O..|..O.|OOOO|...O|..O.|.O..|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 118.53,\n",
-       "  ...O|OOOO|...O|..O.|..O.|..O.|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 77.60,\n",
-       "  ...O|OOOO|.O..|...O|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 108.27,\n",
-       "  OOOO|..O.|..O.|.O..|O...|OOOO|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 2 fitness: 199.62,\n",
-       "  OOOO|..O.|O...|.O..|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 184.38,\n",
-       "  O...|OOOO|OOOO|..O.|O...|O...|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 189.46,\n",
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-       "  OOOO|.O..|O...|OOO.|..O.|O...|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 112.10,\n",
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-       "  .O..|O...|.O..|OOOO|...O|...O|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 160.25,\n",
-       "  OOOO|.O..|.O..|OOOO|...O|..O.|O...\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|...O x 1 fitness: 100.56,\n",
-       "  O...|...O|OOOO|...O|OOOO|...O|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 23.05,\n",
-       "  OOOO|O...|.O..|...O|...O|.O..|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 135.99,\n",
-       "  OOOO|O...|OOOO|...O|...O|.O..|OOOO\t0\tOOOO|OOOO|OOOO|OOOO|OOOO|OOOO|OOOO x 1 fitness: 77.87,\n",
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-       "   'reliable': 31},\n",
-       "  {'trial': 4825,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 2936,\n",
-       "   'numerosity': 3780,\n",
-       "   'reliable': 31},\n",
-       "  {'trial': 4830,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 2937,\n",
-       "   'numerosity': 3783,\n",
-       "   'reliable': 31},\n",
-       "  {'trial': 4835,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 2933,\n",
-       "   'numerosity': 3779,\n",
-       "   'reliable': 31},\n",
-       "  {'trial': 4840,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 2936,\n",
-       "   'numerosity': 3784,\n",
-       "   'reliable': 31},\n",
-       "  {'trial': 4845,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 2941,\n",
-       "   'numerosity': 3790,\n",
-       "   'reliable': 32},\n",
-       "  {'trial': 4850,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 2941,\n",
-       "   'numerosity': 3793,\n",
-       "   'reliable': 32},\n",
-       "  {'trial': 4855,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 2940,\n",
-       "   'numerosity': 3793,\n",
-       "   'reliable': 32},\n",
-       "  {'trial': 4860,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 2948,\n",
-       "   'numerosity': 3802,\n",
-       "   'reliable': 32},\n",
-       "  {'trial': 4865,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 2949,\n",
-       "   'numerosity': 3806,\n",
-       "   'reliable': 32},\n",
-       "  {'trial': 4870,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 2952,\n",
-       "   'numerosity': 3811,\n",
-       "   'reliable': 32},\n",
-       "  {'trial': 4875,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 2954,\n",
-       "   'numerosity': 3816,\n",
-       "   'reliable': 32},\n",
-       "  {'trial': 4880,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 2952,\n",
-       "   'numerosity': 3817,\n",
-       "   'reliable': 32},\n",
-       "  {'trial': 4885,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 2953,\n",
-       "   'numerosity': 3819,\n",
-       "   'reliable': 32},\n",
-       "  {'trial': 4890,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 2957,\n",
-       "   'numerosity': 3828,\n",
-       "   'reliable': 32},\n",
-       "  {'trial': 4895,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 2955,\n",
-       "   'numerosity': 3828,\n",
-       "   'reliable': 33},\n",
-       "  {'trial': 4900,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 2955,\n",
-       "   'numerosity': 3831,\n",
-       "   'reliable': 33},\n",
-       "  {'trial': 4905,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 2957,\n",
-       "   'numerosity': 3835,\n",
-       "   'reliable': 33},\n",
-       "  {'trial': 4910,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 2963,\n",
-       "   'numerosity': 3844,\n",
-       "   'reliable': 33},\n",
-       "  {'trial': 4915,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 2960,\n",
-       "   'numerosity': 3841,\n",
-       "   'reliable': 33},\n",
-       "  {'trial': 4920,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 2956,\n",
-       "   'numerosity': 3838,\n",
-       "   'reliable': 33},\n",
-       "  {'trial': 4925,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 2958,\n",
-       "   'numerosity': 3841,\n",
-       "   'reliable': 33},\n",
-       "  {'trial': 4930,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 2963,\n",
-       "   'numerosity': 3848,\n",
-       "   'reliable': 33},\n",
-       "  {'trial': 4935,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 2970,\n",
-       "   'numerosity': 3857,\n",
-       "   'reliable': 33},\n",
-       "  {'trial': 4940,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 2972,\n",
-       "   'numerosity': 3861,\n",
-       "   'reliable': 33},\n",
-       "  {'trial': 4945,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 2972,\n",
-       "   'numerosity': 3862,\n",
-       "   'reliable': 33},\n",
-       "  {'trial': 4950,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 2961,\n",
-       "   'numerosity': 3849,\n",
-       "   'reliable': 33},\n",
-       "  {'trial': 4955,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 2963,\n",
-       "   'numerosity': 3847,\n",
-       "   'reliable': 33},\n",
-       "  {'trial': 4960,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 2963,\n",
-       "   'numerosity': 3849,\n",
-       "   'reliable': 33},\n",
-       "  {'trial': 4965,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 2956,\n",
-       "   'numerosity': 3844,\n",
-       "   'reliable': 34},\n",
-       "  {'trial': 4970,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 2963,\n",
-       "   'numerosity': 3853,\n",
-       "   'reliable': 34},\n",
-       "  {'trial': 4975,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 2969,\n",
-       "   'numerosity': 3859,\n",
-       "   'reliable': 34},\n",
-       "  {'trial': 4980,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 2972,\n",
-       "   'numerosity': 3859,\n",
-       "   'reliable': 35},\n",
-       "  {'trial': 4985,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 2979,\n",
-       "   'numerosity': 3869,\n",
-       "   'reliable': 35},\n",
-       "  {'trial': 4990,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 0,\n",
-       "   'population': 2980,\n",
-       "   'numerosity': 3871,\n",
-       "   'reliable': 36},\n",
-       "  {'trial': 4995,\n",
-       "   'steps_in_trial': 1,\n",
-       "   'reward': 1000,\n",
-       "   'population': 2981,\n",
-       "   'numerosity': 3871,\n",
-       "   'reliable': 36},\n",
-       "  ...])"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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dP/9n/zAR+VNE9orIQ1nV1cgeU9yMrLgO+Mjb+ovI6QCqugg4Cpzvl/ZqYLK3fycwBOgF1AEOAK8F5N0LaA70946/BRoDtYBlXpk+XvPKOwOnQPorkeWB772yawFXAq+LSIts6nUtcBNQEdiCU0yTgEZAe6Af4OtrNg/o7SfzJuBcv+N53n4yMBKoAXQD+gC3BZQ7BOgCtBCRGsD/gIe9c/4Aumcjs2GUVLoBEcDneTgnu+fJO8DNqloRaIX38QjcC2wDauKsev/GeRtSUdV3gFuABapaQVUf84/3FL2vgBXAmbjnwN0i0t8v2WBgGlDFk+sV4BVVrQScDXySWYVEpD3wLnAzUB2YAEwXkbLAx8BAEanopQ0HriDtmfyxV7c6wGXA0yJyPnlAVWfiLJ5Tvbq3zSTZ9d52HnAWUAF4NSBND5xruQ/wqIg0z4schsMUNyMDItID5xL4RFWjcYrF1X5JpgBXeWkr4hbanuLF3QI8pKrbvH5xY4DLJL1bYIz35XwcQFXfVdUjfunbepawcOBS4DFVPaaqq4FJfvkMwrkt3lPVJFVdDnwGZLd+5PuqukpVk4Bqnux3e/L8BbyMUwDBKWa9vP2ewDN+x6mKm6pGq+pCT4ZY3EPVl87HM6q636vzQGCVqk5T1UScG2hXNjIbRkmlOrDX+7/miqyeJ150Iu7jqZKqHlDVZX7htYEGnkXvJ837epCdgJqq+oSqnlDVTcBbpD1PwCl9X6hqivcsSAQaiUgNVY1T1YVZ5H0TMEFVF6lqsqpOAhKArqq6BaegXuylPR84pqoLRaQe7qNwlKrGq2oMzmp4XR7rlhuGAi+p6iZVjQMeBK4MePY/rqrHVXUFTsHNTAE0csAUNyMzhgGzVHWvdzwZP0uXd3yJ97V3CbDMe3iAU/g+98z5B4E1OIvU6X7nb/XtiEi4iIwT51o9DMR6UTVwX7+l/NMH7DcAuvjK8sobirPOZUXg+aWBnX7nT8B9qYNTzHqKSG0gHPc13F1c/7/KuA7SiEgTz82yy6vD0578WZVbx//Ye0FsxTCMQPYBNSSX/cFyeJ6A+xAcCGwRkXki0s0Lfx7YCMwSkU0iMvokZG0A1Al4Hv2bLJ59HjcCTYC14rqCDMom73sD8q6He5aAeyZf5e37e0DqAPtV9YhfXltwFsH8po6Xt385pUhff/8P1GM4q5yRR6xzpJEOETkNZ2YPFxHfn6wsUEVE2qrqClVdLSJbgAtJ/5AA92Aarqq/ZJJ3pLfr/yV7Nc59cAHuIVsZ514VYA/OjVkXWO+lrxdQ1jxV7ZuHKvqXvRX31Vojsy96Vd0oIsdw7t/5qnrYa5ObgJ9VNcVL+gawHLhKVY+IyN04l0RW5e70r4eISEC9DMNwLMD9R4fgXIw5kd3zBFVdAgwWkdLAHbiPsXqeYnMvTjlqBcwRkSWqOjsPsm4FNqtq42zSBLpfNwBXeW7WS4BpIlI9k8EPW3GjaJ/KIt9PgRdFpC7O8uZTSHcA1USkop/yVh/YnkkeR4FyvgPP41EzK9kzYQdOwfRRH/f83o17hhv5hFncjECG4CxkLYB23tYc+In05vXJwL9wfb4+9Qt/E3hKRBoAiEhNEclu9FdF3IN5H+6h8bQvQlWTcX3BxohIORFpFiDDDKCJiFwrIqW9rVNu+02o6k5gFu6BV8nrXHu2iPi7OefhHvC+/mxzA459dTgMxHky3ppD0V8DLUXkEs+ScBfZWwkNo0SiqoeAR4HXRGSI9xwoLSIXishzmZyS5fNERMqIG2RU2euicBhI8eIGiUgj7yPqEO4ZmJIh9+xZDBwRN1jgNM/610pEOmV1gohcIyI1vY/Ag15wZuW+BdwibnCYiEh5EbnI169NVffgnk3v4ZTHNV74VuBX4BkRiRA3oOFGILO529YDEV6+pXF9cMv6xe8GIiXr0fhTgJEi0lBEKpDWJy7Xbm4jd5jiZgQyDHhPVf9U1V2+DdfJdKify2IKrh/XHD+XKrjOttNxLocjuJGpXbIp7784k/p2YLWX3p87cF/Nu4APvHITALwvyH64PiQ7vDTPkv5hkxPXAWW8sg/gvupr+8XPw70M5mdxDHAf7kv/CO4BOzW7Ar32uhwYh3vBNAYyWCgNwwBVfRE3h9vDOCv8Vtxz4YtMkuf0PLkWiPXcqLfgulaA+w/+AMThrHyvq+qPeZQzGdfvth2wGdiL609WOZvTBgCrRCQO9+y80tf3NyDvpcAI3HP4AM6te31Assk4S+PkgPCrgEjcM/JzXJ/hHzIp4xBuUNXbuPY7ihvU4MP3gb5PRJaRkXdxz+j5uPrH47wVRj4jee9/aRihQ0SexU0IbLN1G4ZhGCUOs7gZhRpx87S18dwDnXFm/rxMDWAYhmEYxYYCU9xE5F1xk/Ct9Au7XNy6bykiEhWQ/kFxkwOuE795b0RkgBe28SRH+hhFm4q4fm5HcS7IF4EvQyqRYRiGYYSIAnOVisi5uP4C/1XVVl5Yc1zHywnAfZ7fHnETpk4BOuOGFP+AGyINrsNkX5yvfQlu5N7qAhHaMAzDMAyjEFNg04Go6ny/6R98YWsA3MCddAwGPvYmTNwsIhtxShzARm8iQ0TkYy+tKW6GYRiGYZQ4CksftzNJPzHhNi8sq3DDMAzDMIwSR7GZgFdEbsJNjEr58uU7NmvWLMQSGYYRTKKjo/eqas2cUxZ+atSooZGRkaEWwzCMIJGX51dhUdy2k37m+LqkzeycVXg6VHUiMBEgKipKly5dWgBiGoZRWPFW8ygWREZGYs8wwyg55OX5VVhcpdNxi9GWFZGGuMkQF+MGIzT2ZmIug5todXoI5TQMwzAMwwgZBWZxE5EpQG/cAsHbgMeA/cB/cOuffS0iMaraX1VXicgnuEEHScDt3izUiMgdwHe4Rb7fVdVVBSWzYRiGYRhGYaYgR5VelUVUppOneovnZlhAV1W/Ab7JR9EMwzAMwzCKJIWlj5thGB6JiYls27aN+Pj4UItSKImIiKBu3bqULl061KIEFbsvCpaSel8ZRQ9T3AyjkLFt2zYqVqxIZGRkZnMelmhUlX379rFt2zYaNmwYanGCit0XBUdJvq+MokdhGZxgGIZHfHw81atXt5dzJogI1atXL5FWJ7svCo6SfF8ZRQ9T3AyjEGIv56wpyW1Tkute0FjbGkUFU9wMwygQevfubXORGXkiNjaWVq1a5Zhm8uTJqcdLly7lrrvuKmjRDKPQYIqbYRiFguTk5FCLkA4RiRCRxSKyQkRWicjjXnhDEVkkIhtFZKo3xyTePJRTvfBF/ms1i8iDXvg6EekfmhoVDwIVt6ioKMaPHx9CiQwjuJjiZhhGBj788EM6d+5Mu3btuPnmm9myZQuNGzdm7969pKSk0LNnT2bNmkVsbCzNmjVj6NChNG/enMsuu4xjx45lyG/KlCm0bt2aVq1aMWrUqNTwChUqcO+999K2bVsWLFhAdHQ0vXr1omPHjvTv35+dO3cGs9qBJADnq2pboB0wQES6As8CL6tqI+AAcKOX/kbggBf+spcOEWmBmzy8JTAAeF1EwoNak3wiq+s9e/Zs2rdvT+vWrRk+fDgJCQmAWwHigQceoHXr1nTu3JmNGzcCcP311zNt2rTUfCtUqJBpWT179qRDhw506NCBX3/9FYDRo0fz008/0a5dO15++WXmzp3LoEGDANi/fz9DhgyhTZs2dO3ald9++w2AMWPGMHz4cHr37s1ZZ51VZBS9uev+IiHJfdCoKgePnQixREZhwBQ3wzDSsWbNGqZOncovv/xCTEwM4eHhzJs3j1GjRnHrrbfy4osv0qJFC/r16wfAunXruO2221izZg2VKlXi9ddfT5ffjh07GDVqFHPmzCEmJoYlS5bwxRdfAHD06FG6dOnCihUr6NKlC3feeSfTpk0jOjqa4cOH89BDDwW9/j7UEecdlvY2Bc4HfFrHJGCItz/YO8aL7yOu49Rg4GNVTVDVzcBGoHMQqlAgBF7vl156ieuvv56pU6fy+++/k5SUxBtvvJGavnLlyvz+++/ccccd3H333bkup1atWnz//fcsW7aMqVOnprpDx40bR8+ePYmJiWHkyJHpznnsscdo3749v/32G08//TTXXXddatzatWv57rvvWLx4MY8//jiJiYmn2BIFy8rth7j+vSU0fXgmg/7zEw0f/IZ2T3zPi7PWhVo0I8TYdCCGUYh5/KtVrN5xOF/zbFGnEo/9rWWW8bNnzyY6OppOnToBcPz4cWrVqsWYMWP49NNPefPNN4mJiUlNX69ePbp37w7ANddcw/jx47nvvvtS45csWULv3r2pWdOtnzx06FDmz5/PkCFDCA8P59JLLwWcQrBy5Ur69u0LONdp7dq187XuecWzjEUDjYDXgD+Ag6qa5CXZBpzp7Z8JbAVQ1SQROQRU98IX+mXrf85JEYr7wkfg9X7yySdp2LAhTZo0AWDYsGG89tprqUraVVddlfobqGhlR2JiInfccUfqx8P69etzPOfnn3/ms88+A+D8889n3759HD7s2umiiy6ibNmylC1bllq1arF7927q1q2ba3mCQUJSMs98s5ZPlm7l2Im0rgMrt6dd6//M2ci9/ZqGQjyjkGCKm2EY6VBVhg0bxjPPPJMu/NixY2zbtg2AuLg4KlasCGQcjZeX0XkRERGEh4enltuyZUsWLFhwKuLnK97Se+1EpApu1ZdmBVWWiNwE3ARQv379girmlAm8vlWqVGHfvn25Su/bL1WqFCkpKQCkpKRw4kRGF+DLL7/M6aefzooVK0hJSSEiIuKU5C5btmzqfnh4OElJSdmkDg2vzdnI+7/GZggvVyacWSPP5bp3F7Npz1HGz97A8B4N+WXjXj5dupWJ10YRFmajYksKprgZRiEmNxaQ/KZPnz4MHjyYkSNHUqtWLfbv38+RI0d44YUXGDp0KA0aNGDEiBHMmDEDgD///JMFCxbQrVs3Jk+eTI8ePdLl17lzZ+666y727t1L1apVmTJlCnfeeWeGcps2bcqePXtS80pMTGT9+vW0bBn8NghEVQ+KyI9AN6CKiJTyrG51ge1esu1APWCbiJQCKgP7/MJ9+J/jX8ZEYCJAVFSUZidPKO4LH4HXOyoqigkTJrBx40YaNWrEBx98QK9evVLTT506ldGjRzN16lS6desGuL5v0dHRXHHFFUyfPj1Tt+WhQ4eoW7cuYWFhTJo0KXXwSsWKFTly5EimsvXs2ZOPPvqIRx55hLlz51KjRg0qVapUAK2Qv8QnJvPeL7H858eNqWErHutH+TLhlApP69F0X7+m3PbRMl76fj0vfZ9mgdyy/xgNa5QPqsxG6LA+boZhpKNFixaMHTuWfv360aZNG/r27UtsbCxLlixh1KhRDB06lDJlyvDee+8BTuF67bXXaN68OQcOHODWW29Nl1/t2rUZN24c5513Hm3btqVjx44MHjw4Q7llypRh2rRpjBo1irZt29KuXbvUDumhQERqepY2ROQ0oC+wBvgRuMxLNgz40tuf7h3jxc9RVfXCr/RGnTYEGgOLg1OL/Cfweo8cOZL33nuPyy+/nNatWxMWFsYtt9ySmv7AgQO0adOGV155hZdffhmAESNGMG/evNRBKeXLZ1Q6brvtNiZNmkTbtm1Zu3Ztapo2bdoQHh5O27ZtU/PzMWbMGKKjo2nTpg2jR49m0qRJGfItbCQlp9DskZk8O3MtqjDh2o789MB5VD6tdDqlDeDCVmfQv+XpGfJYuf1QsMQ1CgHinivFi6ioKLX5o4yiypo1a2jevHmoxcgVsbGxDBo0iJUrVwa13MzaSESiVTUqv8oQkTa4wQbhuI/cT1T1CRE5C/gYqAYsB65R1QQRiQA+ANoD+4ErVXWTl9dDwHAgCbhbVb/NruzMnmGF4b7I6/WOjIxk6dKl1KhRo4Alyx+C3cbJKUqXp39gb1yaqzh23EU5npeUnMItHy5jaNf63PDektTwP54eSLi5TIskeXl+mavUMAwjE1T1N5wSFhi+iUxGhapqPHB5Fnk9BTyV3zIaRZf4xGT+/urPqUrbY39rQY9GuVNwS4WH8fYw946/6/xGjJ/jXKxrdh6m1ZmVC0Zgo9BgipthGCdNZGRk0K1tRujI6/VgLQ8ZAAAgAElEQVSOjY0tOGGKMIeOJ9L28Vmpx2ufHEBE6ZOb2u+efk3pclZ1hr69iItf/4XEZOWpi1txVaf6bN53lG0HjtOrSc38Et0oBJjiZhiGYRhBIj4xOZ3S9sXt3U9aafPRqo6zsiUmu65PD32+koc+T1OwJ/+zC+fk0ppnFH5scIJhGIZhBIlmj8xM3Z99by/a1atyynlWLlc6275tV7+9iIWbsp6yxShamMXNMAzDMAqYlBSl01M/pB6vGzuAsqXyb+WztU8OIFyEsDBhx8HjjJ+9gZ6Na3L75GUAXDlxYa4GPhiFH1PcDMMwDKOAueeTGPYddQMRPr/tnHxV2gBK+00dUqfKaYy7tA0Ap1fqxmVvukmtVTVPE2QbhRNzlRqGUSD07t2bnKblGThwIAcPHuTgwYMZ1jg1ii7+1953jbMjs0XmIeNi9EWVA0dP8EXMDgBqV47IF/dobomKrMYVUW5prwPHCvf6rEbuMMXNMIyQ8c0331ClShVT3Iogqpq6bFV2+K5xSSU+MZmLxv8EwKgBzVjwYJ+gW736tzwDgOV/HgBg6/5jXDFhAVv3HwuqHEb+YIqbYRgZ+PDDD+ncuTPt2rXj5ptvZsuWLTRu3Ji9e/eSkpJCz549mTVrFrGxsTRr1oyhQ4fSvHlzLrvsMo4dy/gymDJlCq1bt6ZVq1aMGjUqNTwyMpK9e/cyevRo/vjjD9q1a8f9998fzKoaeSA2NpamTZty3XXX0apVKz744AO6detGhw4duPzyy4mLi8twju8aAwwZMoSOHTvSsmVLJk6cmC7dyJEjadmyJX369GHPnj0Z8omOjqZXr1507NiR/v37s3PnzoKpZD7yxfLtNHtkJjsOxdOhfhVu7X12SOToUL8qAF//5tqs53M/snjzfno+9yORo782Ba6IYYqbYRjpWLNmDVOnTuWXX34hJiaG8PBw5s2bx6hRo7j11lt58cUXadGiBf369QNg3bp13HbbbaxZs4ZKlSplsJzt2LGDUaNGMWfOHGJiYliyZAlffPFFujTjxo3j7LPPJiYmhueffz5odTXyzoYNG7jtttuYN28e77zzDj/88APLli0jKiqKl156Kdtz3333XaKjo1m6dCnjx49PXZz+6NGjREVFsWrVKnr16sXjjz+e7rzExETuvPNOpk2bRnR0NMOHD+ehhx4qsDrmB79vO8TdU2NSjydcm2+LeuSZquXLAPC/5dv5YOGWDPE+Ba7L0z/wyBcrUVV2HjoebDGNXGKDEwyjMPPtaNj1e/7meUZruHBcltGzZ88mOjqaTp06AXD8+HFq1arFmDFj+PTTT3nzzTeJiUl7IdWrV4/u3bsDcM011zB+/Hjuu+++1PglS5bQu3dvatZ0k4AOHTqU+fPnM2TIkPytV0kiBPeFjwYNGtC1a1dmzJjB6tWrU6/9iRMnUheRz4rx48fz+eefA7B161Y2bNhA9erVCQsL4x//+Afg7qFLLrkk3Xnr1q1j5cqV9O3bF4Dk5GRq166d5yoGk4k/bUrdX/zvPtSsWDaE0kDl00pz6Hgij3zh5nf79JZu1K16Gt2emZOaZvfhBD5YuCVVubuvXxPuOL9xSOQ1ssYUN8Mw0qGqDBs2jGeeeSZd+LFjx9i2bRsAcXFxVKxYESBDfx0btVa88S32rqr07duXKVOm5Oq8uXPn8sMPP7BgwQLKlStH7969iY+PzzRt4D2kqrRs2ZIFCxacmvBBIiVFWfDHXlrUrsQ3/+oZanEAeP+GTlz8+q8ADO1Sn06R1QC3NuryPw/w3i+xPDiwGTe8t4Q9RxLYd/QEL8xaT4cGVTnnbJu8tzBhipthFGZyYQHJb/r06cPgwYMZOXIktWrVYv/+/Rw5coQXXniBoUOH0qBBA0aMGMGMGTMA+PPPP1mwYAHdunVj8uTJ9OjRI11+nTt35q677mLv3r1UrVqVKVOmcOedd6ZLU7FiRY4cORK0OhZ5QnBfBNK1a1duv/12Nm7cSKNGjTh69Cjbt2+nSZMmmaY/dOgQVatWpVy5cqxdu5aFCxemxqWkpDBt2jSuvPLKTO+hpk2bsmfPntT7LDExkfXr19OyZcsCrePJsnLHIfbGneChi4K3YH1OtK9fNct53NrXr0p7rx/czLvPBdwAhp7P/cjVby3izCqn8cvo84Mmq5E91sfNMIx0tGjRgrFjx9KvXz/atGlD3759iY2NZcmSJYwaNYqhQ4dSpkwZ3nvvPcC9VF977TWaN2/OgQMHuPXWW9PlV7t2bcaNG8d5551H27Zt6dixI4MHD06Xpnr16nTv3p1WrVrZ4IQiQs2aNXn//fe56qqraNOmDd26dWPt2rVZph8wYABJSUk0b96c0aNH07Vr19S48uXLs3jxYlq1asWcOXN49NFH051bpkwZpk2bxqhRo2jbti3t2rXj119/LbC65ZbYvUe5++PlRI7+mu9X704N/2TpVkTg3MZFd43QetXK8dxlbi647QeP8+qcDSGWyPAhqhpqGfKdqKgozWn+KMMorKxZs4bmzQvPl3p2xMbGMmjQoKAvNJ9ZG4lItKqGrgd4PpLZM6wo3RdFldy28eH4RB79YmXq3Gw+2tevwvI/0+asKw4rFfy57xjnPv8jANNu6UaU52I18pe8PL/M4mYYhmEYuSQ+MZk2Y2ZlUNqAdErb8O4NgylWgVG/ejnaehMGX/bmAg4eOxFiiQxT3AzDOGkiIyODbm0zjFCx7cAxpgcobCse68dPD5yXehxROowBLc/gkUHFxzr63xs6p+5f+MpPXPvOIn7esDeEEpVsCmxwgoi8CwwC/lLVVl5YNWAqEAnEAleo6gFxQ4heAQYCx4DrVXWZd84w4GEv27GqOqmgZDYMwzCMzPho0RYe+jztI+WTm7vRuaFzG1Y+rXSxcItmReVypdn8zEAaPvgNOw/Fs/NQPD9t2MvPo86jx7POjVqc61/YKEiL2/vAgICw0cBsVW0MzPaOAS4EGnvbTcAbkKroPQZ0AToDj4lI1QKU2TAKBcWx72l+UZLbpiTXvaDJrm0PHUtMp7Sdc3b1VKWtpCAizL2vd7own9IGsHL7IcC5klNS7D4tSApMcVPV+cD+gODBgM9iNgkY4hf+X3UsBKqISG2gP/C9qu5X1QPA92RUBg2jWBEREcG+ffvsJZ0Jqsq+ffuIiIgItShBx+6LgiO7++pwfCJ/e/VnAGpWLEu18mV4fWiHYItYKIisUZ7YcRfxypXtUsPKlnJqxKD//Mwlr/9Cs0dmcta/v+HYiaRQiVnsCfY8bqerqm+BuV3A6d7+mcBWv3TbvLCswg0jf/lsBGz8IXdpW/wd/vZK/pb/xe2w7hsA6pauxLbWd7KnYn0KRTdUEahwOoSFn3pex/ZBYuaTrmZKuWpQ+rR0QREREdStW/fUZSli1K1bl23btmW6jqdx6mR2X/11OJ7OT89OPV7y0AXBFqtQclHr2oSJMLB1bcLDhNsnL+Pr33ayzG9wxpTFW7mxR/EYoFHoUNUC23B92Vb6HR8MiD/g/c4AeviFzwaigPuAh/3CHwHuy6Ksm4ClwNLKlSsrkLotXbpUly5dmi7sscceU1XV2rVrp4Z16NBBVVVHjBiRLu327dt1+vTp6cImTJig6gpO3QYNGqSqqoMGDUoXrqo6YcKEdGHTp0/X7du3pwsbMWKEqqp26NAhNax27dqqqvrYY49ZnQq6Tq921gl39E1fp0eG6PZJN6ev0zk18r9OdSNUx3fUEf1bp6/TpJt1+iND0tfpjr6qM+5NX6dOZ6nOuFcHdTorfZ1m3Ju7OvVvrTrjXu1wdq20OlUrrzrlan2sV5n8vU5j2+W+Tjt/z/W9BywtyOdZMLeOHTuqEXoajJqRus1ZuzvU4hRq3v5pkzYYNUOf+GqVtnx0pjYYNUOTk1NCLVaRIS/PrwKdx01EIoEZmjY4YR3QW1V3eq7QuaraVEQmePtT/NP5NlW92QtPly4rbB43I8882xBaXQoXvZB9us9vhdifYWQ+rxP5QlNo0h/+Pj5/8z1Vti6Gd/rCNZ9Bo3ywNuS2nU+C4j6PmxFc1u8+Qr+X5wPw3KVtuKJTvRBLVHS48f0lzF77Fy9d0ZZLOpQ86/jJUJjncZsODPP2hwFf+oVfJ46uwCF1LtXvgH4iUtUblNDPCzOM/CUpAUrlYhHoUmUhKQ+uvlyXHw+lCmG/LV+bJCXkT365bWfDCDEbdscBcGOPhqa05ZGnLm4NwD2frCBy9Nds/MuWs8tPCkxxE5EpwAKgqYhsE5EbgXFAXxHZAFzgHQN8A2wCNgJvAbcBqOp+4Elgibc94YUZRv6SW8WpVET+KTHpyi+kCo2vTfJLWS2sCqph+PHbtoPcPnkZlU8rzegLm4VanCLHGZUj0g3guOCl+USO/ppbP4y2wTX5QIENTlDVq7KI6pNJWgVuzyKfd4F381E0w0hPchJoci4VtwKwuKkWXoUmPy1ueWlnwwghf3/1F8CNIi0dXggGCBVBBrauzQc3dubadxanhn27chc3TlrKm9d0pEwpa9eTxVrOMHyKWK5cpRGQnOCUrfwiORHQ4m9xy0s7G0aI+Hjxn6n79/VrGkJJij49G9ckdtxFrH6iP/Pu7w3AnLV/MXH+H6EVrIgT7OlADCO0xB+CI7sDwrwh7Lm1uAHs3wTVz859uQlxcDjj2oYAJB7NffnBxlffg1thz/q0sKoNcp9H4nF3fl7a2TBCwMFjJxj9PzfwKObRvlQpVybEEhUPypUpRYPqpRjztxaM+Wo1L8xaT6fIanQ5q3qoRSuSmOJmlCwm9IIDmzOPi6iU8/m+NP/pACPmwJkdc1fuB0Ng25Lc5V2YKF0OwkrBzy+5zcfQz6BxLkeZfnIdbJiVdlwY62kYwN1TYwDodlZ1U9oKgOu7N+TLFTtY/udB/jFxIQD392/K7ec1CrFkRQtT3IySxZFd0ORCaH1Z+vDwMtC4X87nt73KWZBmPZzRcpdTuQ16QNQNmceHl85d+cGmVFm4/hs45M2DfWw/fHs/xO3KfR5HdkLtdnDOnblvZ8MIMqqaunD69d0jQytMMebz27rzwLQVfLJ0GwDPf7eOd37ezKJ/97H+hLnEFDej5OAbBHBG64yKW24pUx4a9XWKW176fSXFQ80mJ19uKKnfBbdcMBD3l1Pc8lT3BDi9ZdGsu1FimL5iB0kpyr19m9C/5RmhFqdY89xlbbm/fzPemPsH89b/xR97jrJ+9xFa1qkcatGKBKbeGiWH/BoEcDIjLZMSikffrpOqeyEdMWsYwNGEJL5Yvp1/fRxDg+rluM3cdkGhZsWyPPq3FrwzrBMA7/y02dY3zSVmcTNKDqmjGk9RiTiZkZZJ8cVjNOVJ1b2QzlFnlFh+/WMvNSuUZfXOwzw2fRUHjyUC8NrVHQgPkxBLV7JoUL0cAP9bvp35G/aw9OG+IZao8GMWN6Pk4LMSBdvilpICySeKh9Up3OuwXcwtbiJST0R+FJHVIrJKRP7lhY8Rke0iEuNtA/3OeVBENorIOhHp7xc+wAvbKCKjQ1EfI40pi//k6rcW0ffl+fzr45hUpQ2g1Znmqgs2IsLw7m4x+r1xJ9h56DhxCUnEJyaHWLLCi1ncjJJDfs0jlqq45dLqVJzmLxOB8DxOQpyUkKbwFR2SgHtVdZmIVASiReR7L+5lVU232KqItACuBFoCdYAfRKSJF/0a0BfYBiwRkemqujootTBSUVU6Pz2bPUcyfnRM/mcXOjWsFgKpDIBH/9aCPs1rMfTtRXR7Zk5qeOy4i0IoVeHFFDcjNGyaCwe2pB0f/QvKVvImuD3hpqCQLAzCleu67c+FbmqJqpGw87f0acqUhxZDILwUrJ/lRjYe3ePiTtX6E+4pYFsXQ/SkzNOUKQ8tBrsBEb9/mj/lFhZKRcCOGFd3/3b2sXUJ/OWnlxRBi5u3VvJOb/+IiKwBzszmlMHAx6qaAGwWkY1AZy9uo6puAhCRj720prgFmVU7DqcqbeFhQqs6lbisY10qnVaacxrVCLF0RqfIagxsfQbf/J42Yn1vXAI1KhSDD958xhQ3I/gkJ8KHl0LKSXZElTCo0wG2L80+XcUzoFYLmHx5+vBK2b1/c0F4KahYB9Z97basKFfd1fWru9xx5bqnVm5hoUo92DzPbeDaObJHWvynw+Dw9vTnFOG6i0gk0B5YBHQH7hCR64ClOKvcAZxSt9DvtG2kKXpbA8K7FLDIRiYs3LQPgPn3n0d9r1+VUXgoUyqM14d2ZG9cApMX/clL368nauwPzLu/Nw2qlw+1eIUKU9yM4JMU75S2c++HjjfAsb0w4VwXd83/4MNL3P7ITIwSMZPhx7FuWoqw0pDi9U9peQn0G+v2d69yylpCHJyIc2H9xro0pSKgfD7M1n3nUjh+MPO4PWtdHU7EpfUFGz7Lm1ajGPDPH9x8bv7t7E/CEWh/LfR+0B2HhTvlrggiIhWAz4C7VfWwiLwBPAmo9/siMDyfyroJuAmgfv36+ZGl4RGXkMTYr9cAUK/aaSGWxsiOGhXKcsd5jXjpe7dSS6/n57LmiQGcViY8xJIVHkxxM4KPT5mpcDpUPtO523xU8XthVc7EMlahlvuNPwSnVUlzf5arlpY+/pBXTnxaWRVrZ57fyVKmfHq5/Uk85pWf4Fd+0VRcMqX0aa4t/dvZn6R4KF8jf9s7BIhIaZzS9pGq/g9AVXf7xb8FzPAOtwP1/E6v64WRTXg6VHUiMBEgKioqHxfDNR6fvip1X8RGjRZ2wsKEdWMH0PThmQDc80kMb1yTy1VqSgA2qtQIPr4Xva/Dun+n/Zw6sfvSJhxyfeJSzyubMU1SQmgGBvgPXihOAxMCyWx0rW8EbXjRrq+4t/s7wBpVfckvvLZfsouBld7+dOBKESkrIg2BxsBiYAnQWEQaikgZ3ACG6cGog5HG9oPHAVj9RP8cUhqFhbKlwtnw1IUAfLtyF4fjE3M4o+RgipsRfFKn5fA6rKdTunLoxO6vAEVUzjw8neIUUFYw8J/rLL+mICmMZDa6NrnY1Lc7cC1wfsDUH8+JyO8i8htwHjASQFVXAZ/gBh3MBG5X1WRVTQLuAL4D1gCfeGmNILH/6AkWbd7P7eedTbky5mQqSpQOD2Pitc7Sdufk5azcfohNe+JyOKv4Y3exEXwCrVBhft8POb3w/RWwdIpbRMb9kFvcEkKjOAYL/3b2kV+THIcYVf0ZyMyn9k025zwFPJVJ+DfZnWcULLNW7SI5RbmwVe2cExuFjn7e8mPz1u9h3nrXNWbUgGbc2vvsUIoVUsziZgSf7F7u+W5xC4EikZnFrYi7DjMlM4tbcbYwGkWSWat3U79aOVrWqZRzYqNQ8vrQDumOn525lriEkrs8llncjOBy4hjMuMftZ/Zyz0+L2++fwsYqucs3P/H101v5OWiKOw4rht9I/u38lxuxlzqKt4hb3IziQVxCEnPW/sXf29axQQlFmIGta6dOxvv58m2MnLqC71bu4tKOdVHVEndtTXEzgsvOGLcB1GyaFt7yYqh/jpuZv8kAOL1V5udXbwy1WkLiUZdu7wY3ue6Zfl9kYaWgUV/Ys85N2XFGa6jSoODqFIgINB0Iu7x+600GBK/sYOLfzrE/p4VXbwy124ROLsPweHPuHwC2KkIx4sJWtRk5dQX3fuo2gMX/7kOtSiXnY1FUi9+o86ioKF26NIfJWY3Q8Mcc+OBiGP4d1O8aammMYoSIRKtqVKjlyA/sGXZqxCcmMz1mBw9+/jtlS4Wx6vH+Jc4qU5yZOP8Pnv5mberxwNZn8PrQoj1dSF6eX2ZxM4KL9YEyDKOAGf3Zb3wRswOAK6LqmdJWzLjp3LMZ3r0h63fH8fZPm/jf8u3MW7+HXk1qhlq0oFAMO94YhZpiMurQMIzCSWJySqrSBnBvvyYhlMYoKEqFh9GiTiWuOycSgGHvLuat+ZtCK1SQMMXNCC5mcTMMowA4Ep9I1NgfaPzQtwD0bFyDL2/vTsWI0iGWzChI2tWrwsv/aAvAU9+s4YfVu3M4o+hjipsRXMziZhhGAfDJ0m3sjUubU/CVK9vTtl6VEEpkBIuL29dlSLs6APzzv0tZvHl/iCUqWExxM4JLcZ6Q1jCMkOFvaXn87y2pVj6H5fOMYsX/Xdk+df+KCQtCKEnBY4qbEVxmP+l+zVVqGEY+setQPAs27eO+fk2IHXcRw7x+T0bJInbcRVQs68Zc3vphNDN+25HDGUUTU9yM4HLiCERUgTLlQy2JYRjFhGnRWwHo3bRWiCUxQs2se84F3ML0d0xeTu/nf2Ti/D8oTlOfmeJmBI+UZPfb9dbQymEYRrEhdu9RXpi1HoDmtW1Zq5JO7cqn8flt56Qex+47xtPfrGXBpn0hlCp/sXncjOBhI0oNw8hn/rd8OwA39mhIeJjN12ZA+/pViR13EbsPx7M3LoGLxv/M1W8tSo0vUyqMbmdVZ+J1HQkX4e6pMQxpdyYXtDg9hFLnHlPcjOBhI0oNw8hH9sUlMH72BhrWKM8jg1qEWhyjkHF6pQhOrxTBc5e24YHPfksNP5GUwrz1e2j68MzUsBm/7WTV4/0pX7bwq0UhkVBE/gWMAAR4S1X/T0SqAVOBSCAWuEJVD4ib8voVYCBwDLheVZeFQm7jFEk+4X7N4mYYximyLy6BjmN/AKDb2dVDLI1RmLmiUz3OrlWByqeVom7Vcrz+40YWbtrP4tj004Z8vnw713QN4rrWJ0nQFTcRaYVT2joDJ4CZIjIDuAmYrarjRGQ0MBoYBVwINPa2LsAb3q9R1DCLm2EY+cTIT1ak7j81pFUIJTGKAh0bVE3dv6df03Rx8YnJNHtkJv+Zs4GrOtcv9C73UAxOaA4sUtVjqpoEzAMuAQYDk7w0k4Ah3v5g4L/qWAhUEZHawRbayAeOep1DzeJmGMZJoqo89fVq5q/fA8DXd/WwtUiNUyKidDhXd6nP7sMJ9HlxbqjFyZFQKG4rgZ4iUl1EyuFcoPWA01V1p5dmF+DrJXgmsNXv/G1emFGUOLIb3j7f7ZepGFpZDMMosvx1JIG3ftoMwOB2dWhZp3KIJTKKA6MvbAa4UagtH53J4fjEEEuUNUFX3FR1DfAsMAuYCcQAyQFpFMjTpCsicpOILBWRpXv27MkvcY38Im6X+23+NzirV2hlMQyjyLLtwHEAalQoy0tXtAuxNEZxoVJEaRY+2AeAoyeSaTNmFvPWF05dIiTzuKnqO6raUVXPBQ4A64HdPheo9/uXl3w7ziLno64XFpjnRFWNUtWomjVrFmwFjLzjmwqk4/XmKjUM46Q4mpDEsHcXAzB5RJdC3xfJKFqcUTmCFY/2Sz0e9u5iPlgQy+H4RGau3ElKSuGYxDckipuI1PJ+6+P6t00GpgPDvCTDgC+9/enAdeLoChzyc6kaRQUbmGAYxiny7cpdxCUkARBZ3VZfMfKfyuVK88fTAxl5QRMAHvlyFW3GzOKWD5fR/snvmb1mdw45FDyhWjnhMxFZDXwF3K6qB4FxQF8R2QBc4B0DfANsAjYCbwG3hUBe41SxxeUNwzhF7vvUjSS9IqouZUrZwj9GwRAeJvzrgsZ8eGMXIkqHUcGb2+3Q8URunLSU579bG1L5QjKPm6r2zCRsH9Ank3AFbg+GXEYBkmpxMzepYRh5Z92uI6n7z13WNoSSGCWFHo1rsPbJCwFITlFemLWON+b+wWs/um3itR3p1/KMoMtlnyxGcDCLm2EYJ8GhY4l8t2oX/f9vPgBTb+oaYomMkkh4mDBqQLN0feBu+iCaDk9+z1HPfR8sCv/aDkbBcWw/xP4E9btBhVq5P++w18WwUi6m00s6AetnwuK33LFZ3AzDyANXvrWQNTsPpx53OctWSTBCh68P3Hu/bGbs12vYf/QEd01ZTsfIqpQvU4pBbWpTvULBvudMcSvJ/PQiLHgV2g2FIa/n/ryX3Hw3jDmUc9r1M+GTa9OOI6rkTUbDMEosSckp6ZS2Jwa3DKE0huEIDxP+2fMsbuzRkLFfr+Gdnzcze62bCOO7VbuYPKJgrcLmKi3JxB/0fnOhgJ10GX553/IzRFQquLIMwyhW/PrHvnTHQ7sU/nUkjZKDiPDvgc2J8ltOy/9Do6Awi1tJxtfvzPdbIGXEp+1XsYeuYRi559UfN1IxohTT7+hBtfJlbN42o9ARHiZMu/UcAF74bh2v/riR6C0H0q2Nmt+Yxa0k41Oq/JWrfC/DTym0gQmGYeSSvXEJLN68n3Mb16RhjfJUPq10qEUyjGy5uddZAFz6xq8cOl5wS2aZ4laSSbW4FaTi5pd3uD14DcPImfjEZKLG/gDA7ec1CrE0hpE7KkaU5rbeZwPQ9vFZrNxeMN2QTHEryQTb4ibm5jAMI2fe/zU2db957YqhE8Qw8sgDA5pxRVRdAF6Yta5AyrA+biWZYPdxMwzDyIbE5BSmLtnKuG/dzPTRD1+A2AefUcR47rK23Hl+Y6qWL1Mg+ZviVhhISYHFE6FRH9i2FNpdVXBlHdwKW36FpgNg6yIX5q9cRU+Cv9ZAWDh0HuEGFEy/A1KS3VQeYeFpaZf9Fzpcl7GMP36EjT9Ap3/C5vkFVxfDKCBEpB7wX+B0QIGJqvqKiFQDpgKRQCxwhaoeEKddvAIMBI4B16vqMi+vYcDDXtZjVXVSMOtSVNgbl5DqHgW46/xGBT4flmEUFPWqlSuwvE1xKwysnwkzR6UdtxgMZQroor8/EA7+CZe/nxbmb3H7+l7n0kw+AadVhbPPg+UfurgyFeBEXFrar/6VueI2/3nY8gtEVIYEb2h0PZvt3ChSJAH3quoyEakIRIvI98D1wGxVHScio4HRwCjgQqCxt3UB3gC6eIreY0AUTgGMFpHpqnog6DUqpCzevJ/E5BRith5MFz6yb5MQSWQYhRvr41YYSDyW/jj5RMGVdXiH+z3uPSTP7pNmcUtOgpRE6HkfSHXv3ogAACAASURBVJgL91fq/pn2NczZfUBTQDVjGb78Eo+7PNteDTd+l/91MYwCQlV3+ixmqnoEWAOcCQwGfBazScAQb38w8F91LASqiEhtoD/wvaru95S174EBQaxKoSY+MZkrJixg6NuLeP471x/ogQFN+ePpgeYiNYwsMMWtMFKQfc58+CbGjaicVl6y91s6wk3dEdg/zX+5qojK7jezPmz+feeS4m2ZK6NIIyKRQHtgEXC6qnprvrEL50oFp9Rt9TttmxeWVbgBfPP7zgxht/VuZPO1GUY2mOJWGAlGh/50ilu8s5z5LwRfqmxGBdJ/HrbsFLfE42lxSfE2f5tRZBGRCsBnwN2qmm5KdFVVnPszv8q6SUSWisjSPXv25Fe2hZYf1/3FPZ+sSBf2+W3nhEgawyg6WB+3wkiwLW7g3LM+JaxU2TSLW0pS2jmZKm6ZyGoWN6MYICKlcUrbR6r6Py94t4jUVtWdniv0Ly98O1DP7/S6Xth2oHdA+NzMylPVicBEgKioqHxTCAsrT3+9BoB/9mjI7iMJNDujIu3rF9xs84ZRXMhScROR38nma1JV2xSIRCWRwH5iwba4+cpMVdz8LG7pVj7IravU18ftmFMIzeJmFDG8UaLvAGtU9SW/qOnAMGCc9/ulX/gdIvIxbnDCIU+5+w54WkR8Gkk/4MFg1KEws3L7ITb8FUf9auUYfWEzSoWb88cwckt2FrdB3u/t3u8H3u/QghOnhJISsDRGMC1uZSumlZnqKvWzuPkrZnm1uPlGlJrFzSh6dAeuBX4XkRgv7N84he0TEbkR2AJc4cV9g5sKZCNuOpAbAFR1v4g8CSzx0j2hqvuDU4XCyatzNvDCrPUAnN+slilthpFHslTcVHULgIj0VdX2flGjRWQZbhi8kR8EWq2CYXHbPM8pYqVPc8e7fk8bcRpeFsLLwOov3ebDfw43n+L2wcVw+yKnAP4/e+cdHld55u37mSLJsmXZcse9O+CAMaZXAwkQSEhCSGCzX1jCQvKFtN0viSFlYdnNpu4mkGzYAKGlQEg2CY4NoZhim27ANu69yU1ykWyV0ZTn++OcM3NmNL1oRtJ7X5euOeedd855z8xo5jdPXfw12PMWdB2z7tv5mnVrLG6GXoaqLgdSRchfkmS+EvuRm3jfg8CDxVtd7+VYZzAq2uoH+PnaZTPLvCKDofeRTYybiMi5qvqKvXMOJqmhuDgWKvGChktrcRsxCw6ssVyYNfUwbLo13rIb2g9Z22NOtubtXx173Kf/17o9/2uWC3TyBVadt9ZGOLwNxpwCqx6H2gaYcQXUjbaEoNdvFRY2GAz9ngt/9FJ0e9UdHyzfQgyGXkw2wu2zwEMiYptYOGqPGYqFY2H77DPwq0tLa3FzW798NTDcFm5uV+ngsTD3/8B7T1j7H/slTL/U2r7kO7HHX/MA/Oaa+Gb1s6+BS+8o3foNBkOvpC0Q4nCbVaPyzW+ZH3MGQ76kFW4i4gGmqeopjnBT1dK0u+/POMKnZrC930NN333Vsfgzd+kOkQSBlyJGzZkT6rRaYkWCxi1qMBiS8uZ2K7Tv9itmMbLOfE4YDPmS1uWpqhHgG/Z2ixFtJSLUacWUOfFmJRVuCckGXke42RY3R6S5xVoqMRYVboH4UiIGg8GQwLp9VrLSdWdMKPNKDIbeTTaxas+LyNdEZLyINDh/JV9ZfyIUsEtwuCxYpTyXg68avD7w+LoXy83K4ua21rmK9xoMFYqIDBURU8qoDCzf3Mys0XXUD/CXeykGQ68mmxi3T9m37owpBaYUfzn9FKdIrc9l/SrluRzcIs1Y3Ax9FBF5CfgI1ufd28BBEXlFVf+5rAvrR3z5sXd5bdshPnHauHIvxWDo9WQUbqo6uScW0q9JZ3ELdVkN371FaHIR7IxvaB8VbtXQdRy62vKzuAXb44v3GgyVRb2qtorIP2I1gr9DRFZnfJShKBxu62LhKqvU0AC/N8Nsg8GQiazUgIjMBk4Eot/KqvpoqRbV73Asbt4qe9+2uO18DR663Nq+cAHM/2b+53juX+CVu+PH/LX27UB4+2Fre8wce2yAa97A5Md0xhd+KTZWVZv/Gg2G0uCz21N9EvhWuRfT3/iu3dpqaK2fL108rcyrMRh6PxmFm4jcgdVr70Ss6uBXAMsBI9yKhWNxc7I5HevV4a2xOa/cU5hwa9oY2x53Bsy8AqbZJT4+cg/sfdfanmg3ea6ph48/YFniRqQokjlwGAweB617YmPOMQ2GyuFfgWeA5ar6lohMATaXeU39hg37raSEt751qemSYDAUgWwsbp8ATgHeVdUbRWQU8JvSLquf4W7E7vQIdcajFNhz2n2ssafB+a7wnqnzrb9ETr4283Fnfwxe/VlsvyqFdc5gKAMi4gXGu3srq+o24Jryrar/0BWKsHZvK9eeNs6INoOhSGTzn9RhlwUJichg4CAwvrTL6mc4FjeIt7gVM0mhVAkP7pg2MfErhspCVcPA9eVeR39lyfoDAJw+yRQiMBiKRTYWtxUiMgS4Hysj6zjwWklX1d8IdcaavXtTWNy0iBa3Qq13btyJC16T5m+oSF4RkZ8DvwfanEFVfad8S+ofvLypidoqLx+bO7bcSzEY+gzZZJV+wd78HxH5GzBYVQvKyBKRfwL+EUtBvAfcCIwBHgeGYQnE/6OqXSJSjRVPdxpwCPiUqu4o5PwVR6gTBo6wtn3VyS1u4QItZu5jFSoC3cRlkabqyW0wlBU744a7XGMKXFyGtfQbwhHl8bd2c+bkBvzGTWowFI1skhN+DSwFlqnqhkJPKCJjgS8DJ6pqh4g8AVwHfAj4iao+LiL/A9wE3GvfHlHVaSJyHfADYrXl+gZx9dNqUsS4FXqOEhX1NeU/DBWOqiYJ4DSUmr/aJUBOmzi0zCsxGPoW2fwMehDLGvYzEdkmIv8rIl8p8Lw+YICI+IBaYB/Wr98/2vc/AnzU3r7a3se+/xIR6Tumnc4WaN4UX1PtkJ1NemRH/Nzty2DbyxDs6H6c1n1WHTaASAR2vwlbX4S2ZqusSFtzadZvCu4aKhwRGSUivxKRp+39E0XkpnKvq6+z+L19AHzRlAAxGIpKNq7SF0VkKXA6MB/4PHAScHfaB6Y+XqOI/BjYBXQAz2K5Ro+qasietgdwgiLGArvtx4ZEpAXLnRqnRETkFuAWgAkTelEvvOfvtG5rh1m3wQ5o3mi5M3e9Hj/3kaus24u/Axd8Lf6+/5oFo0+Gzy+DXa/Bwx+yxt3JDiNmQdMGGH9G8dZfOzy2PetDxTuuwVA8HgYeIlbDbRNWvNuvyrWgvk4wHOHljU2cM3UYtVVFKB5uMBiiZOMqXQIMxEpIWAacrqoH8z2hiAzFsqJNBo4CfwAuz/d4Dqp6H3AfwLx584oYxFVi2g9bt/Pt75RpF8PBtRDushIVJl8IKGxfat0vXug4kvxY++3Qww77mE4PUoepl8Cn/whDipgUPONyuPlFq97b2HnFO67BUDyGq+oTInI7RH8Ahsu9qL7MtqY2usIRLp41stxLMRj6HNm4SlcDXcBs4GRgtogMSP+QtFwKbFfVJlUNAn8CzgWG2K5TgHFAo73diF1+xL6/HitJoW8QCsDo98c6DgwabY93WgkJQydBw9TY/JrBmUt7OPfXDIkf91UXV7QBeDwwdi5MvsB0TTBUKm0iMgw7nVpEzgJayrukvs2b262P6Dnjh2SYaTAYciWjcFPVf1LVC4CPYwmmh7AsZfmyCzhLRGrtWLVLgHXAi1jFfgFuAJ60txfa+9j3v6BazLTIMhPqTN4X1Gnc7u5h6qsB34DuiQbhUPy+c39Nffy4SSQw9E/+GetzZKqIvIKVpf6l9A8xFML6/ccYWOU1iQkGQwnIxlX6ReB8rHIcO7CSFZble0JVfUNE/gi8A4SAd7FcnIuBx0Xk3+0xJ/7kV8CvRWQLcBgrA7Xv4C6+C/GN5p1sU7H1ta86vrNC9BidyfdrBsePm0QCQz9EVd8RkQuBmVg1azba1n5DiVjb2MLJ44bQl/LIDIZKIZuo0Rrgv4C3XckDBaGqdwB3JAxvA7pFzatqJ5BF76VeSqgTal1VxR3hFuyMWdwc4eatiq/zFj1GopCz96sThZuxuBn6DyJysaq+ICIfT7hrhoigqn8qy8L6OOv3tbJqTwv/cM6kci/FYOiTZJNV+mMROQ/4P8BDIjICGKSq20u+uv5AN4ubbRULHIvtO8INydHilugqNRY3Q7/iAuAF4MNJ7lOs+FpDkfnJc5sA+MCJo8q8EoOhb5KNq/QOYB6Wm+EhwI/VZP7c0i6tH6BqZZCOnBUbc0Rc657YflS4aXx5D4d1T8bvR5MTjMXN0K9x0q9/parLy7qSfkRHMMyY+hrOnTY882SDwZAz2WSVfgz4CHaPP1XdC9SVclH9hlY7cTbsCrdxrGJO+Y/qOhgx09qecHZyi9vSH8bvhzqtUiJxCAwzhTAN/Yob7dt7yrqKfkR7V4g3tx/mspNGl3spBkOfJZsYty5VVRFxUukHlnhN/Yeuduv2xKtjY45VrLPVup10HgybCnfa1Qt+e23sPgeNWLdDJ1m3jvt15pXw7m+ssTsLSQQ2GHol60VkM3CCiLj7KwugqnpymdbVZ1m6qZlAKMIHjZvUYCgZ2Qi3J0Tkl1h11m4GPgvcX9pl9RMcl6c79szZ7rSFWqJ7M2mMWyDhtjOWgWow9FNU9XoRGQ08g+U1MJSYHz5jtbM+fXJDhpkGgyFfsk1O+ADQihXn9i+q+lzJV9YfcIRWsnIgKYVbQoybamw/ehuIr/9mMPRTVHU/cEq519EfUFW2NVn9kv3ebKJwDAZDPqQVbiLiBZ5X1fmAEWvFJp3FLdDa/T5nP9wV23dvd7O4GeFm6L+IyBOq+kkReQ+7a4JzF8ZVWlRaO4M8+uoOANPmymAoMWmFm6qGRSQiIvWqalrEFJu0FrfW7vc5+26LW1T82R0VVF0WN+MqNfRrvmLfXlXWVfQDTr7z2ej25y+cmmamwWAolGxi3I4D74nIc9iZpQCq+uWSraq/kCnGTbzgTXiJfDXxMW7R0h/1cHw/RELG4mYwAKq6z95sBjpUNSIiM4BZwNPlW1nfoqU9vgnFzFGm6IDBUEqyCUT4E/AdYCnwtuvPkC3NW+Dp2yASiR+PCrckFreuY8mFl68auo7D7//essq5hRvAe3+ArS/EW9y6lQYxGPoVS4EaERkLPItVTPzhsq6oD/H0mn1x+/W1/jKtxGDoH2STnPBITyykT/P430HzRpj3WRgxIzYedZW6hJV/AMz5NBzaCmPndj/WtA/Apmdg/V/h7C9CrV3kcsYHrXMs/6m1P3wa1I2BEz8aX27EYOh/iKq2i8hNwC9U9YcisrLci+or/Pr1nQA8+08X4DGtSQ2GkmNSf3qCSIoWr8ksbiLw0V/ATc/A5d/r/piJZ8MVP4w93jnGuDNg4MhYUsPcGyw36ycfgdmJrRoNhn6FiMjZwKeBxfaYN4sHPSgiB0VkjWvsThFpFJGV9t+HXPfdLiJbRGSjiFzmGr/cHtsiIrcV8brKzuYDx1i7t5XPnD2RGaPqmDbSuEkNhlJjhFuPovG7ySxu2eAIvVBXfIKDryZ1UoPB0H/5KnA78GdVXSsiU4AXs3jcw8DlScZ/oqpz7L+nAETkROA64CT7Mb8QEa+dmf/fwBXAicD19tw+wStbmgG4+fwpZV6JwdB/yCY5AQARqVXV9lIups+Tqjl8riLLEXpui5tTcDfYFj/HYOjnqOrLwMsAIuIBmrNJrlLVpSIyKcvTXA08rqoBYLuIbAHOsO/boqrb7PM/bs9dl9NFVChPrNjDiLpqxjfUlnspBkO/IaPFTUTOEZF1wAZ7/xQR+UXJV9aXEDvww11zzb2fa/JA1OIWiBd/yZIcDIZ+joj8TkQG2+361gDrROTrBRzyiyKy2nalDrXHxgK7XXP22GOpxns9e492sG5fK03HApknGwyGopGNq/QnwGXAIQBVXQVcUMpF9Vnc9decfW8VeHL0WMdZ3Fzu1riyIka4GQw2J6pqK/BRrDIgk7EyS/PhXmAqMAfYB/xnUVYIiMgtIrJCRFY0NTUV67AlY/Ueq7TnNy6fWeaVGAz9i6wUg6ruThgKl2AtfZ9uwi2Qn8CKWtw601jcjKvUYLDxi4gfS7gtVNUg3QJOs0NVD6hqWFUjWD2bHXdoIzDeNXWcPZZqPNmx71PVeao6b8SIEfksr0d5d9cRfB7h02dOLPdSDIZ+RTYxbrtF5BxA7Q+/rwDrS7usPobY+njJXTDtUggH4acnw7G9tHkGcdJti6NTd3z/yszH89sC7ekFMTesv8YqJeJghJvB4PBLYAewClgqIhOxei/njIiMcRX2/RiW6xVgIfA7Efkv4ARgOvAmVnut6SIyGUuwXQf8XZ7XUVEsWr2PC2aMoH6AqdtmMPQk2Qi3zwN3Y8VlNGIVsLy1lIvqczRMgeZNVhYoWJmfx/YC8GpwRpoHpqC6Dq76CRyx6icxcAQMmQgXfB1GnQTDpoHXfJgaDACqeg9wj2top4jMz/Q4EXkMuAgYLiJ7gDuAi0RkDpbFbgfwOfsca0XkCaykgxBwq6qG7eN8EXgGqwTJg6q6tkiXVja+/Ni7NB7t4JPzxmeebDAYiko2BXibseofGfIlbLeEcdyaLpfp4vBZ+R1z3me7j0040/ozGAxxiMiVWKU63LEJd6V7jKpen2T4V2nmfxf4bpLxp4Cnsltp5fPoaztYuMr64Tlz9KDyLsZg6IdkFG4ick+S4RZghao+Wfwl9UGcBILobUy4BYi3jIXCEXxeU17PYCgWIvI/QC0wH3gA+ASWG9OQI8FwhH950jIYfv7CqVx20ugyr8hg6H9koxBqsDKoNtt/J2MF2N4kIj8t4dr6DomWNlc9t0ThFggl9DM1GAyFco6qfgY4oqr/CpwN5BGjYPjzO1ZexZUnj+G2K2YhYnpcGQw9TTYxbicD57riNe4FlgHnAe+VcG19hxwsboFQhIEmr8BgKCYd9m27iJyAVdpoTBnX02t5bdshAL770dllXonB0H/JRrgNBQZhuUcBBgINqhoWEVN5MRvSWdw0UbiZSisGQ5FZJCJDgB8B72AlFjxQ3iX1TlbtPsr504czpLaq3EsxGPot2Qi3HwIrReQlrNT2C4D/sKuQP1/CtfUOIhE4st3KHE3lNnCEmoatRIW2g9G7AsR/AHZ0FV+4HW3vYn9rJ12hCNNH1jGgyuqvHY4orR1Bhg40H8KGvouq/pu9+b8isgioUdWWdI8xdKe1M8i25jY+PrdPNH4wGHot2WSV/kpEniJWaPKbqrrX3i6kbUzf4N1fw1+/DJ98FE68Ovkcd+Hdna/CC7HEsxYGxk19aWMTU0YUL1MrElHm3PVcdP8Tp43jx9eeAsBdf13LI6/tZN1dl1FblXXbWoOhVyAiH09zH6r6p55cT2/nB09vAGDW6MFlXonB0L/J9tu6E6u9Sw0wTUSmqerS0i2rF9G8ybo9mthcwkUoAOPOgD1vQmcLeP10+QZxXdvX2KWjmD9zBLdd8T4u++lSIppXQfeUdIVjyQ4egcNtsX6pf11t1RFt7wob4Wboi3w4zX0KGOGWA9ub2wC4YEbld3UwGPoy2ZQD+UesbgnjgJXAWcBrwMWlXVovIRuhFeqEQSPt7QCEAuwdfi7vHLcS286dNpzJwy3LW7GzSgPB2PEGD/AnjaELR4orFg2GSkBVbyz3GvoKX338XV7deojLThpFlc+UKzIYykk2/4FfAU4HdqrqfOBU4GhJV9UrSSF+VCEcgJoh1r7dGD7siaWOVvs8+L2CCASCxY1xcwu1wTX+OCEXnZNkzGDo7YjIP4vITUnGbxKRr5ZjTb2Vv6x0Cu4aN6nBUG6yEW6dqtoJICLVqroBmFnaZfVCnO4IiTiJCTX19r7VGD7siSUEVPu8iAjVPk/xLW4ht8XNl/T4JpPV0Ef5NPBokvFfA0lajxiSsaYxlsfx2XMnlW8hBoMByE647bFT6f8CPCciTwI78z2hiMwUkZWuv1YR+aqINIjIcyKy2b4das8XEblHRLaIyGoRmZvvuUuDbWkLpaiM4iQmRIVboLvFzW+9DNU+bwmEW4LFLYlIM0V/DX0Un6p2+0Wlql1YGfKGLLjqZ8sBWPSl80wZEIOhAsgo3FT1Y6p6VFXvBL6D1avvo/meUFU3quocVZ0DnAa0A38GbgOWqOp0YIm9D3AFMN3+uwW4N99zl4QkRXWT3p9ocfO6LW6e6G2xrV+dLjdo/QC/sbgZ+hMeERmVOJhszJCaap8HEZg9tr7cSzEYDGQQbiLiFZENzr6qvqyqC+1frMXgEmCrqu4ErgYesccfISYOrwYeVYvXgSEiUjlVz/e8Zd2u/C28eT8cXB9/f5O9X22X+Hj5BxAO8E5jR3RKtc+qq1bt9xQ93swt1OpqfOw81M725jZe2dIczTA1MW6GPsqPgMUicqGI1Nl/FwGLgB+Xd2m9g52H2giEInz9MhMdYzBUCmmzSu3uCBtFZIKq7irB+a8DHrO3R6nqPnt7P+D8Kh4LuGtt7LHH9rnGEJFbsCxyTJgwoQRLTUGr1buPtiZ46mvW9p2u2p5v21p06CTrNmyJpf1tsWSGE4YMAErvKnXqw92zZDN/frfRNccIN0PfQ1UfFZEm4C5gNlZcw1rgX1T16bIurpfww2c2AvDxU8eVeSUGg8Eh25ZXa0XkTaDNGVTVjxRyYhGpAj4C3J54n6qqiORUo0JV7wPuA5g3b17P1beIZHAzRkKWaJt0Hkw6H3YsAyCMhxXfvpSBVb5oJ4NSuEodUfbHz5/NvEkN/PaNnbQFQglzjKvU0DexBZoRaXny+larN+no+poyr8RgMDhkI9y+U6JzXwG8o6oH7P0DIjJGVffZrlCnL1QjMN71uHH2WGUQDsLAkXFtrOIIuUqBeOP7kg4Z4MfnjXmrS5JVartBB1ZbL3XDwOpu5zAWN4PBkIiqcqiti78/qwc9GAaDISPZJCe8DOwA/Pb2W1iNmgvlemJuUoCFwA329g3Ak67xz9jZpWcBLS6XavkJdcYSD1Ld7+v+a9UrxIk2sF2lRY9xC9vHjiVAtHclWNxMjJvBYEjgmbX7AWjpCGWYaTAYepKMwk1Ebgb+CPzSHhqLVRokb+wG9R8gvuXM94EPiMhm4FJ7H+ApYBuwBbgf+EIh5y4q4ZDVOD6tcAuAr7rbsM/TvRpBtb8ErlJblFX7Y+7YY53GVWowGNKzbt8xAL5hEhMMhooiG1fprVgN5t8AUNXNIjKykJOqahswLGHsEFaWaeJctddQeSTWaEs1J8n9Xm8S4VaSAryJFjcvrR3BhDnG4mbou4jIt1X13+3talVNUXTR4KCqLFzZyJzxQxjfUFvu5RgMBhfZFOANuMt/iIiPlP2d+hmJNdpSzcnW4laSrFLb4uYIN7+H1m4WNyPcDH0PEVkgImcDn3ANv1au9fQmXtt6iB2H2rl2nskmNRgqjWwsbi+LyDeBASLyASxX5V9Lu6wK4ehueOn7cNFtMGR89/u3PG/dJgq3J26AYIcl2I7ugtHvt8Y9safb6+mumat9nhL0KnWEW8xVejwxq7TI5zQYKoQNwLXAFBFZZu8PE5GZqrqxvEurbFbusdpRX/o+U6vYYKg0srG43QY0Ae8Bn8OKOft2KRdVMax6HFb+BlY/nvz+bS9Ztyd/Coa74kDW/QU2P2PdP2QCTLvUGj/nSwQHT2BXZASv1V/R7XBWjFuxs0rDiIDfds06As7B5xFjcTP0VY4C38SKj70IuNsev01EXi3Xoiqd44EQP/ybpWtHDTZlQAyGSiMb4fZRrM4F16rqJ1T1fjvurO8TsWPBUtVqC3XCsOkw8Wz44ptw2X/E3z/zCrj1dTjlU9b+5AvY+KnlXNB1N5+e373laqlcpVbLGke4xV7yBz4zj4HVyRvPGwx9gMuAxcBU4L+AM4E2Vb1RVc8p68oqmIdf2V7uJRgMhjRkI9w+DGwSkV+LyFV2jJsB7Pg11y/SxFi2JLFtUdel39vtvlIV4HVb2ZyG9s52Kc5pMFQCqvpNVb0Eq5zRrwEvMEJElotI/wj3yIO9LVbS1au3XVzmlRgMhmRkU8ftRmAa8Aes2mtbReSBUi+sVxDqjBdnifXaktRvS8zydFPt8xIMK+FI8QyagVA47lzVcQV/vSXpj2owVBjPqOoKu7vKHlU9D7ix3IuqVHYdaueU8UOirfgMBkNlkY3FDVUNYrWNeRx4m1gD+L6N4yKNpChA2c3ilijc0ljckgk32xrWVUTXZSAYibOyeVzZrFU+T0ncswZDJaGq33Dt/oM91lye1VQ+uw63M8GUADEYKpZsCvBeISIPA5uBa4AHgNElXldlELbLfYRSlH3qZnFLdJUmsbgF47M83Thirpiuy0RXqRuPWOfsNFmlhn6Cqq4q9xoqmVA4wp4j7UxoMNY2g6FSySZe7TPA74HP9bvClaFMwi0fi5vtKvV318w1dtxbMS1gia7SREpR9NdgMPROfvfmLiIKoSKGaxgMhuKSTYzb9ar6F0e0ich5IvLfpV9amelqhzfvs7bf/CXcWQ93DbNu7zkVju3Pz+KWxlXqt+PP3t11tCiXsK+lg+fXH2TPkY7omNv65vc6rlJjccuWP7+7h0v+8yWOtHVlnmww9DJ2HWoH4KwpwzLMNBgM5SKrDFERORX4O6xiltuJ7zHaN2lOUp/TiXU7vA0Obe1ucRs7D87+IgwcAa174X0f6XaIxIK4bmaPHQxA0/HiGDYXr94HQIurxdU1p41la9Nxhg+qZtboOqr9HtraTBPpbPnp85vZeaidXYfbGTqwqtzLMRiKhqqydm8rtVVezplqhJvBUKmkFG4iMgMri/R6oBnLXSqqOr+H1lbZhDq7W9yqauGy76Z9mNOlIJmr1MniKmUnMRaGowAAIABJREFUg5F1Nfz42lOi+1a3BuMqzRYn49e4lw19jRc3HuS1bYe45YIpKeNiDQZD+UlncdsALAOuUtUtACLyTz2yqkogU43hUKC7xS0L0maVRpMTek4UGFdpbjhvC/OcGfoaX318JQCfu2BKmVdiMBjSkS7G7ePAPuBFEblfRC4BundG76uk6pbgkMzilgWOKKvydn/qnbGeFW4mOSEfjJWy7yMiD4rIQRFZ4xprEJHnRGSzfTvUHhcRuUdEtojIahGZ63rMDfb8zSJyQzmuJROqSmunFTIxbFBun2kGg6FnSSnc7ISE64BZwIvAV4GRInKviHywpxZYNsIZ4sxCndacnC1u4bgWVG5EhKoe7mRQiv6o/QHznPULHgYuTxi7DViiqtOBJfY+wBXAdPvvFuBesIQecAdWu60zgDscsVdJNB+3km2uP2NCmVdiMBgykU1WaZuq/k5VPwyMA94FFpR8ZeUm1Jn+/s5W6zZXi1swkrk8Rw9ac6p93pLG1PVVjKu076OqS4HDCcNXA4/Y248QK0Z+NVZPZ1XV14EhIjIGq1/qc6p6WFWPAM/RXQyWndv+dzUAV8zuHyU6DYbeTFadExxU9Yiq3mf3/+vbBI6lv7+zxbrN0eJ2oLWTqrTCzepkcOh4gN2H29FMsXYFUu3z0NYV5nggRChceVakUDhCZzDM0fYujgfKn/3a1hWyb8OmcHH/ZJSq7rO39wOj7O2xwG7XvD32WKrxiuGtHYdZsuEgAFNHDirzagwGQyZMw/hU/OEf0t8fFW7ZW9yOtnfx9Jr9DB+UuoxEtc/DrsNtnP7d54koLLh8Fv/3oqlZn8ONU0RzWpoPY0dEzr7jGcY3DGDZNyqrsfQHfrKU7c1t0f0/f+EcTp1QHk9TVyjC0XartMp3/rKG7/xlDa/edrHp6dhPUVUVkaL9shKRW7DcrEyY0HMuy1W7Y3Ujx5r3ssFQ8RjhlolP/dZym7bsBvFC/Vj442fzsrgdtou2XjN3XMo51X4Pe4504BQuP9CawWWbBsfl+ofPnZ1yjrjyTXYf7kg5r1y4RRvA2zuPlE24tXd1t/jtPNRuhFv/4oCIjFHVfbYr9KA93giMd80bZ481AhcljL+U7MCqeh9wH8C8efN6rHXBvy9eD8A73/lAT53SYDAUgBFumRg3D+oS4j7+dAt02r9Sc7C4OQHtc8YPSTmn2udlf0tMQBUSSxUIhfF5xBSKLRImIcEALARuAL5v3z7pGv+iiDyOlYjQYou7Z4D/cCUkfBC4vYfXnBUN5nPCYOgVGOGWiWTCzFeTl8UtWsMtSfFdh2qfJ5qWD4WVnbAazOcUxmhIgykB0r8QkcewrGXDRWQPVnbo94EnROQmYCfwSXv6U8CHgC1AO3AjgKoeFpF/A96y592lqokJD2XF6xEumjGi3MswGAxZYoRbJpIJM191fsLN6ZqQpip5tc8Trc4PhVl5AqEw1X5TAb1YJLN+KqYZd19FVa9PcVe35Cy1sohuTXGcB4EHi7i0oqGqCDBzdF25l2IwGLLEmGMy4c1kccvdVZq2HIhLaHk9UpirNEPpEUNuOK+f1xOLCwyGjXAz9F4OtXURiijDTdFdg6HXYL7VkxF2BaF7kjxF+Vrc0jSYd3ALrcE1vgItbka4FROn/Mfgmpih2tTAM/RmNh2wyh5NH2XKgBgMvQXzrQ5WA8oNi2H7Mms/0Jp+fp4Wt92H24HMMW4OdTV+lm1uzruWm9WlITdXaanrxuWCu0yBg/McloP9dobv4AH+6Fh/SVhQVZ5Zu58tB4+XeymGIvKfz24CYMYo4yo1GHoLRrgBHFwHj/8dPHIVBDth5yvp5/uqwYltysHitmqPJUSGpcneckpLDPB72WWLlMff2p1yfjoCoUhakQhwzrRhcfsb9mcoPNyDXP3f3V+H9WVc34odRwAY0g+F29q9rXzu129zw4NvlnsphiLy9k7rPT2yzrhKDYbeghFuEGtfBRDqsMQbwP99Lfl8t1jLweImwPBB1WmbON92+SyWfWM+r98ei3/ekVDLLFuyiXE7fVID6++6nJ9+ag5ARXQnSOQH17yftf96GTNH1eHp3uK1R/F5JK4gcn9pfXXMznRuPFp5tf4MhTF95KCkvZMNBkNlYrJKIb4vaSgQ269OEffhFms5xrgNrfWnnePxCOMbarM+ZvrzhamtyvwSD6jyRi19lVjyYuKwgQys9jFycHVZhWUgFGFIbRU1rgSSSny+SkF/Eaj9Cacg+KdOH59hpsFgqCSMxQ0ssRbd7owJt1SiLE+LWzauy2KSS3KCM69SvqDdfVOdtVX7PGUVSlbMoCcubrC/uEr7y3X2J55Zux8wpUAMht5GWYSbiAwRkT+KyAYRWS8iZ4tIg4g8JyKb7duh9lwRkXtEZIuIrBaRuUVfUDeLmy3kUomyvC1uuScLFEIuQtGZVylf0O51OM9Ztc9bVmHpPJ/u57RShG6pqZT3haF4rN5jJVidOXlYhpkGg6GSKJfF7W7gb6o6CzgFWA/cBixR1enAEnsf4Apguv13C3Bv0VfTUxa3Hq6rlotQdOZVihBxC4Uqt8WtjALCev28ca9hfxE0puxJ32NHcxtzJwyJ/n8ZDIbeQY//x4pIPXAB8CsAVe1S1aPA1cAj9rRHgI/a21cDj6rF68AQu7lz8UhlcfOmyP50xJq3GnII6s21rlqh8cK5CMWoq7RCYrbcAtJnZyRU+8ss3JK5Sivk+So1/UWg9icaj3Ywdmhx4mkNBkPPUY6fWpOBJuAhEXlXRB4QkYHAKFXdZ8/ZD4yyt8cC7noYe+yx4rHz1dj20h/DhkWWVS2VcnIsbp7sczvWNLbwXmNLTq7SKq/18rhbYGXL7sPtHDwWyFm4vbm9Mtoo7mjuXq+t2uel6ViAfS3lyWxctrnZFm6x53Tp5iaWbW7iX/+6ltbOYFnW1RO84Xpf/NuiddFixIbeyZaDx9h1uN28jgZDL6Qcws0HzAXuVdVTgTZiblEg2vcvJ7UiIreIyAoRWdHU1JTbijrtQq8NU2DrC1Zdt3Qu0AlnwaBRMOODWZ/i93YttjOnNGT9mLuvOxUArzd305sTeHzSCfVZza+3a5Md7agM8bH5YKxe25ghllBuPm5ZQm9+dEWPr8exAIYiyoi6amaPHQxAR1eYb/15DQ+9soMVOypD9JaCVtf74lfLtyctjmzoPby2zXqvXjO3uL+BDQZD6SmHcNsD7FHVN+z9P2IJuQOOC9S+PWjf3wi489XH2WNxqOp9qjpPVeeNGDEitxWFAjDudPjyuzDpXGssXdLB7Gvga5vg2oezPkVnMMyY+hpuPHdy1o+5fPZoBlX7COXRD9NxbX301Ow+mH1eD6dOGEIwXBkusS57/avv/GDUSum4TI+09by4dJ7PK2aPpsbvZdGXzue608cTDEdosUVNX3abdiW4So3rtHez72gHfq9w6ftGZZ5sMBgqih4Xbqq6H9gtIjPtoUuAdcBC4AZ77AbgSXt7IfAZO7v0LKDF5VItDqFATKhFb4tbSTzfvqFWQH7u7oxAMIwI+HOw1pW73IabWF/X2HPmt13H5RCXzvPiXk9iskRfFjOJ78G+fK39ge3NbYxvqMXnNYkJBkNvo1wFeL8E/FZEqoBtwI1YIvIJEbkJ2Al80p77FPAhYAvQbs8tLuEAVA21th3BlkOZj2zoCkXyKgVS7fN0s3ZkgyMUc6mIXu3zVoyr1BEGVa4vFudSusog3Jxzul/Dar+XrlAkmpWXz+vUW0h8zvvytfYHtje3MWX4wHIvw2Aw5EFZhJuqrgTmJbnrkiRzFbi1pAvqEYtbmJo8iu9W+715WTcCeQhFy+JWGcHKTgZnMuFZDqug87y4a7g51lDHqlkppVRKQeJz3pevta8TiSg7DrVx/vTh5V6KwWDIA2MnB6scSNTSVhqLWz5CCvJ3XzrCJ6dz2RakSiBdKZNyiIZkrttqn4eIxrJ++7L7MPHa+vK19nWeX3+AzmCEUYOL+xlnMBh6BiPcIIXFrQTCLR+LW94xbrmfr9wFbt1Yz1dyoZtHdZSCiQk3l6s0WrQ4EnfbF+kW41YhlllD7ry69RAAl500uswrMRgM+WCE2zPfgpbdsWK70eK6KYrv5sHi1ft4e+eRuHitbKnyeXhxY47lTSjAVVohLrBkFkN3c/eebjb/z79fCRBXZd7ZDtlK8uk1xcuZ+e7iddz+p/eKdrxCeGfXEQ60BuLG/vPZTWVajaFQjnWGGDW4mvENpviuwdAbMcKtfjyc9HE45Xprf/Y11t/p/1i0U9z6u3cAGFide0ihI1ZCOQbk5+Uq9XkrKqu0JsHi9rXLZka3dx/uXqC3VKgq25rbADh5XKwu3pQR8cHdXk/x/p3uX7adx97cVbTjFcI7O48AcP9n5vG5C6fg9wrHelg4G4rHzkNtTBxmEhMMht6KEW5nfR6ufQgmnGntn3AqfOJBmPWh4p9qSu7NnJ0A4lzdcPmUHyl3Syk3yWLcBtf4eegfTrfu78F1Ouf6xuUzqavxR8enjhgUN69S4gOLjXP9F84Ywe1XvI9b508DrCB3Q+9j5+F2Jhprm8HQazHCrQfJp/doYhxVtjgN0XM7l4eucKQivpBTWQxjPVV7zqWbLL7NvZbYvMpwMxebxJqAzvNQjrIshsJoC4RoOhZgkikFYjD0Woxwq3CiQiVHURAIhfNITqicL+RUMXrONfWsxc0uBZIg1BKTJ0rhZs7VRV4KEmsCxsRz+ddmyI1ddojBBGNxMxh6LUa4VThRoZLjl2RertIK+kJOlYWbrwWyoLUk6ZqQbL9Ya7JKF1pUooiOiee+aWHsy+w8ZMVqTjIxbgZDr8UItwonb1dpPlmlFfSFHAhmcJX24Bqd5z4xWcLpnQpQV+Mr2ppCLld1ZYjo+NeiHOLZUBx2HrItbsOMxc1g6K0Y4Vbh5CtUjrR35ZVVap2rfF/IncEwqkrz8a7krlJ7rLMHBY1TeiTx+XR3dRhc4y9aiZJK63+aWBOwHOLZUBy2Nh1naK2f+gH+zJMNBkNFYoRbDzJiUO5ttDKJqR8/s5FJty2Oc69Num0xR9uD+PN0lV533+s5r7MY3LNkM7O+8zdu+9/3aD4eYGB1d+FWa4/9ddXeHlvX9fbzka6cS43fgyo8sGwbk25bzJaDx/M+nzvx4qzvLeHLj72b97Hy4cz/eJ6rf76cg8c6mXTbYv70bmNC4WHrfZKreF7T2MKk2xbz5vbDRV2vIXtW7DzCnPFDyr0Mg8FQAEa49QAzRg3C6xEued/InB+bKcbt5y9uAZLHQv39mRNzO5f9hdx4tCOnxxWLP7y9G4A3d1hf7F+4aFq3OcNt8VtblXv7sHzpCIYZO2QAp09qSDnHKfVy/7JtgFW0Nl8SRfrCHhSpAAdaA6za08K2prboWJyr1J+fZXbZ5mYAlmw4UIRVGnKlpT3ItqY25k4YWu6lGAyGAjDCrQcIRZQrZo9O2jA9E9m6pZJ9iU7OMeU/VYupnqa1I8iMUYNSVnY/eVx9j7kQnazOT50+Pq5rQiLOc12MdVWCezSRxB6tYFylvY01e1sAmDvRCDeDoTdjhFsPkE9NNYds486SWeTSCY3k56qMt0NrZzDt89WTrbkcS2am52awXZi3GMkElSiIkrlKK1FgGlKz+cAxAGaMqivzSgwGQyFUxjd1HyffBvOQi8XNuj/sykj0enKz8FWKcAuGNe1aerI1V6pSIIkMHmDFvxVDdFVCJimAK2wyITnB/jFRIes0ZMfmg8cZUutn+KDi9WE2GAw9T2V8U/dx8ukb6pBtHTfH+lFI26VcLXSlJJ3QtSxuPSTcnK4JGdzIjsWtGE0nKsWSFXTFTcbHuBlXaW9k88HjTBsxKK+QDYPBUDlUzjd1HyafmmoOubpKC/kyFSrnAz2tq9Tfc67SVF0TEhmcWF6hAAFXKYLI/Z4zrtJ4RGSHiLwnIitFZIU91iAiz4nIZvt2qD0uInKPiGwRkdUiMrcca95y8DjTRw3KPNFgMFQ0RriVGFWlK48uBg7O45xWNalwvuyPdRanllgu7GvpYN3eVrYcPEZnkXqIZnKVbjqQf7mNXEjVpzSRwTXxwi0YifD2ziMcaetKOv9oexfPrTvAO7uO0BkM89rWQ1E3d7JSIi0dQSIR5ZUtzRxo7QSsxIkN+1tzvqZ0uFtsPbduf3Q7WQHe5XaWaDZ0BsO8uPFgVnPXNLbwwoYDdHRVhoBNw3xVnaOq8+z924AlqjodWGLvA1wBTLf/bgHu7emFHjoe4HBbF9NGmvg2g6G3Y4RbiYm52vJ7qp1q/W/vTF9ewjnPa1sP5XUegBF1udeZA6vu24fuWcal/7WU2//0Xt7nd9OW5kvb+ULf39JZlHOlI1OM28WzrBIvDQlxQ7sPd3DNva/yT0+sTPq4nzy3iZsfXcHHf/Eq/754Hdff/zrLt1hC6KFXdgCxpu4Av3hxC+/sOsKnH3iDW379NgA/fnYTl/90Gdub27odP1/cNdaeWLEnut3psq7V1VjxfAtX7Y22UMrEH97eEz12OstuKBzhIz9fzmcfXsHv39qV09orgKuBR+ztR4CPusYfVYvXgSEiMqYnF7bZ/jEwfaSxuBkMvR0j3EpMthabVHg9wqzRdVR5079U0Rg322Ly4tcuyvlcI+qquXrOCQyuSV1oNhlOGx2AV7dmb4VJxxWzR6e873L7vtbOYFHOlY6oqzSF8P7Fp+fy+u2XMKjaFye0mo4FAFi6qSnp41o6YmtfufsoYJVBAes1nzthCG9961IWfek8677OYPQxm/Zb2YFv77SE0KHjgfwuLgnHUnR/uPyk2OtR4/fy3Y/NttecnYW31XW9EU3tRw6EItE4wdYyWI9zQIFnReRtEbnFHhulqvvs7f3AKHt7LLDb9dg99liP4VhxpxnhZjD0enL7hjbkTLYxUukYXV+T0uUWPY/tonQEXMPA/DLHRg2uIRguQoR9Hri/z+vSiMdBdgeDnshqzCS8a/xeRtd7o3OCYUtsZBKVcW2t7OtwXKVdoQhTRgxiSG0VQ2qrGN8wgEAw0iMxZanOkdjFYmKDU7cuO3dmIBjGI1BX44/rDJHu/JUS65eC81S1UURGAs+JyAb3naqqIpLTP5ItAG8BmDBhQvFWiiXcBlZ5GVNfU9TjGgyGnsdY3EpMtuUk0pFNFqVzf6FC0amRpmmsIqXCncXoS1PKpCezGnN5Pt1z3Bam5Mft3o/UOVeypu6BUKTb9TovUTFfqVSiKlG4Oq9Btm2vnASdGn/697L7Giu53IiqNtq3B4E/A2cABxwXqH3rBPU1AuNdDx9njyUe8z5Vnaeq80aMGFHU9W4+eIxpo+pMRqnB0Acwwq3EZFtOIh3OF3c25ylUKFb7PETU6vbQ08RfYxrhlmWmbTGIPp9ZxCjGCbcMbr44gRKKt5YmFmx2xHQqIRMqooU01XOaeP25dk9wahlmei+7r7FSs1ZFZKCI1DnbwAeBNcBC4AZ72g3Ak/b2QuAzdnbpWUCLy6VacsIRZeWuo5x0wuCeOqXBYCghxlVaYorhKq32edK6l9znCYQiVPk8ef+ydosif4a4umKTrYWlJ1su5RKjWOMS5xktbkHrdeoKRbqJ7sSCzY7FNZWQSdanNl9SCjdfonDLTTw7VsRMXS96iat0FPBn+3/MB/xOVf8mIm8BT4jITcBO4JP2/KeADwFbgHbgxp5c7I5DbbR1hTnVNJc3GPoERriVmNgXfwHCLYN7CeLruBV6Lut44WgsWU+gqll/UWdblLgY5CK8q+IsbpldpYNr/DQfD8S9dpGI0hWOdHeVBru7SqPHKlIJFmcNyejmKs3V4mZbEav9nrSvW7wlsjItbqq6DTglyfgh4JIk4wrc2gNLS8rqPVbyy4nG4mYw9AmMqzQH1u1t5T+eWs8za/dnnmwTc10W5io91NYVFwMG8V0S/vyuFTJTSLFf61y5FVdN/OI+0BrgP55az5rGlpzOG4po1l0HetJV+uTKvfY5c3OVHk+Rnbm16Tjfe3o9jUc7om2yOmzh9ce393C4vcs+lstV6vfw5o7DPLfuQHR+MBxhhV0i5okVe9iwv5VHXt1RUOcMSC2Gu1ncbPG8ancLh9u6+MOK3ckeFjuuXcuw2ueNlj1J5O2dR/jIz1+J7r+SYp4hN97acYS6ah8zTY9Sg6FPYCxuOfDQK9v5w9t7GN8wgMtOSl2uwk2mchLZ4Lgs1zS2cOqEodFx55c0wHu2ULIsG4W4ZXMTRat2dxdo9y3dRtOxAD/51Jysz5t4vtMmDk0xs2ddpe/sssRRt84ISTh1wlBWN7agCjU+Lx3BMPUJj3vsjV08sHw7A/xePnjiKLY1xeqg7TjUzuvbrDp8g1xZtc71rnDV8nveFnEAz68/wNJNTXSFIxwPhLh1/rQ8rtQiEIogAhMbajlhyADOmz6c376+i+GD4mv8Da21spb3t3Typcfe4ZUthzhjcgMThw1Mcdww1X4PHV1hq+RHRPEkJKBcc++rcftH20tf7qU/sL2pjemjBuHr4dAHg8FQGsx/cg44lpFcXHTFcJVeOMPKMEvM4HP2z58+HLCKlzpfkPmSqyhK1Skh1w4KjrvvrqtPYsf3r0xbDLgnWy6pwucunJJVvN+dHzmJ7d+7kh3fv5L1/3Y5158xvtvjOoJhhg+qYv2/Xc5XLp0eHa+xXzOnLtrZUxqi9zli+uwpw/jdzWcCcMQWNXdfN4cx9TXROLeWDLF1mQiELBf5S1+fz+9uPosvXDSNV267mAFV8VbcGr+Xk04YTCgSiRZCTrQIxx/XsgR/+JQTgMxxeRfMKG5WZX9mW/NxJg839dsMhr6CEW45ECvbkI9wK8B9maL8hbPvWIO6wpHCXaU5xo+lei5yFVW5CFwnQ7fUMW5W3F1hfWYTnwf38dzHdVpmObFxyXqDOq7GxHmF/ChIJJfrTSxTk66CjGMJjoruDK/d4BofoYjGteAy5M6eI+0caA3wvjHGTWow9BWMcMuBxHpbWT0mWJysUvf5E9fjfOk7RVp70lWaMmA+RzdmLgK3p1yljlWo0Jp4btyvj/u4jvh2slHjkhP8zvyYSIvO83sKEuqJ5OJqd5ImHNIVbo5mlWZZg8/9Y8SQP9/+yxoAzpw8rMwrMRgMxaIswk1EdojIeyKyUkRW2GMNIvKciGy2b4fa4yIi94jIFhFZLSJzy7FmiO9OkG2B2kJ7lUJqMRWzuPmi9weCBWaV5pEtmMt4yuPkkL3p8wgeKb2rtFA3t2ORcr9XAsFwNPs0TrjZMW3JLW62hc7viblUO2MCr5D3ViK5uNqtbOfu9eiSH9fOKs3yh4HzYyTbAr+G5Dgxk7OMxc1g6DOU0+I2X1XnqOo8e/82YImqTgeW2PsAVwDT7b9bgHt7fKU2zpeNanrrQrLHFCXTMyFuzBFHUYtbKExnKFJwsV/IrSJ+LuMpj5NDoVsRyaoocaHE1pSnq9Tv7fZeCbheH3eweMziFrIfG1/HzbmNukqdeRXkKk3fEcEpwJutxc2X1TxDeoYPsvoP93RNRoPBUDoq6b/5auARe/sR4KOu8UfV4nVgiNNWpqfJpzhoUQrwRt1LKVylA/zR/YItbjm2kyqHqxRsa08R65cloxjtw9zHcbaTHS8xxq3K6xZuMaEXdZW6LW7FdJXm4GpPFM/pOyLECvBC5h8Gbve/IT+6QhH2Hu1gYkNtuZdiMBiKSLmEmwLPisjbdmNlgFGuNjD7saqTA4wF3EWi9thjPcrPX9jM+n2t0f3NB49n9bji9Cq1vpi//Zc1/PndPbFjO67SmljT9a6CY9y6B48fPNbJpx94nc88+CaH27q4+dEVfOwXr/B/f/M27V3JxdOmA8dzqimWq0jyez088trOtJmMhVIMV6n7OK9ubeb1bYeTZtzWOa7SjiBVXk9cqQxHTAux98JLG5sAKxvVvb6lm5ryWuu7u47wmQffZOmmprhCwumo9nloPNLBVrukyU0Pv8W+lo6kc6OuUtva+O6uI3H3H0soWOw8H5VahLc3sOdIOxGFCSlKtBgMhl6Kqvb4HzDWvh0JrAIuAI4mzDli3y4CznONLwHmJTnmLcAKYEV9fb1iiUMFdMWKFbpixYq4sTvuuENVVceMGRMdmzt3rqqq3nzzzXFzGxsb9f03fjdu7PPf+r7aFdGjf1dddZWqql511VVx49O/9ZT+8pe/jBtbuHChNjY2xo3dfPPNqqo6d+7c6NiYMWO0oyuk9ede3+2avvaLP8U//ivf0HO+t0QHDh2R1TUtXLgwbuyXv/yl7jva0e2aXthwQAdMPT1u/OQ7n9GGy74YN3bt7ffoDT/7W1bXpKp6xx13xM0dfcNP9fHFL2X1Ol398+U66JTLsrqmbF8nVe32Oo245jv6myXvFnRNT724PG5s5IWf7nZNU9/3fp24YJGecNZV3a7py9+/P26s4bIv6sQFi+LGxp58rk5csEhHzT4n4zUle++dOP9jOnHBIq0aNTXjNSX7f6o/93qduGCRNowYlfG9N/YLj+hNd90bN/at7/1X92s65Tzd3nQ8q9fJft1XlOPzrBR/p512mhbK39bs04kLFumKHYcLPpbBYCgtuXx+lf0DCrgT+BqwERhjj40BNtrbvwSud82Pzkv1V4wPvUTO+d4S/effr9TGI+06ccEifeyNnVk97s6Fa3T2v/ytoHOHwxGduGCRTlywSOf/6MXo+I/+tkGn3L5Y39h2SCcuWKTLNzfpaf/2rN7+p9V5n+vw8YBOXLBIH1y+LTr29Hv7oud/9NXtOnHBIr3xoTd14oJF+oXfvq3Tvrk47hh/XdWoExcs0k37W7M+76JVe3XigkW6YV92j/nLu3t04oJFuuXgsazPkSvv7DysExcs0hfWH8jr8c4at9prvOqeZTpxwSJ9/x3d3w/v7TmqExcs0tP//Tn3RJhKAAAgAElEQVSde9ezcfc5z/k3/7Q67r0wccEi3d/SEZ332Yfe1A/dvTSvtd7y6FvRY9738tasH/fkSuu1Pv8HL+jEBYt04crGpPOmfXOx/uDp9aqqOv2bT+n37W2H17Y268QFi/SVzU15rV81tw++Sv8rxmfY955ar1NvX6wdXaGCj2UwGEpLLp9fPe4qFZGBIlLnbAMfBNYAC4Eb7Gk3AE/a2wuBz9jZpWcBLRpzqfYY3YOrsw/eLzTrL7HCfOzY8XFDgVC48M4JSeLp3DFarZ1WUPxgl2uvex/L3FtS5eoqzbYeWCEU21WazdzWzmDa83k8khD/Fl82pBiuxVzer875q9JcaziiBMPqql/XvV9pMbKvDfG8s+sIJ42tp6aAZCWDwVB5lKPl1SjgzyLinP93qvo3EXkLeEJEbgJ2Ap+05z8FfAjYArQDN/b8kt11qLzR/aweFyysIG76NUXia2MFCy/A64gC9xer+8vYqR8WzYLsDHXvY5lHnbVcv7hj4rB0CQqFiolcBKw7mzdTFmu1z+OqMRdfNqQYz0cuQtVn/6hI95p3JTyPiWVEwF3v0IiMYtAVirB6z1GuP2NCuZdiMBiKTI8LN1XdBpySZPwQcEmScQVu7YGlpSVWhyrXzgKFZXmmPXYwvjZWRzBMV7gwi5vP68HnkYRMSJdws4PInay/Yx3dLUT5WMNy/eLuibZXhYqJVGVcNNncJOU/ElHX3GMBa9udSJDMkpUP+Vxvutc80ZqaqqOEe46hMFbuPkpnMMJZU0zhXYOhr2E+JbNAVaPZmrkWfw2EIlln6WW1lrhjh+Pct8c6u9cAy4du9blcwsOpH1bnKhibaCGKWSVL19M1VYmUYlKwqzSHNVb7Mgs3B6/Lde7eTnzdckFdb6x8rjeddTGx1EuydRaj3qEhhpO1e/qkhgwzDQZDb8MINxvVZHYQC7fLLFb8NYyqEonEJVokfWwhBXETcZe/6EiojRVrl1TY+ar9XjqC4eh1JbW42a7SFrt8Rdzj83CVduZYNiUqFGxR6Q7cLBadBVvc4sWMJrW1xc8t6Hz+zK7SVM9RxDWWj/B3fpx0BsOEI/HHTyyJU+Xz0BkME4lo9C/6XJsYt6KwramNYQOraBhYVe6lGAyGIlOOGLeK491dR/jUfa9zz3VzuHy2Vdv3poff4uCxAH/90nndrAEdwTD3L9vO/cu2xx3nv/9uLleeHF8buNCCuInsOdLBotV7eWfnUZ5ZewCPxCxcb2w/DMTijvJlgN/L797Yxe/e2EWV1xPXL9IRh0NrLeEWDGuc1Qfi3ZhTv/kUX7hoKv/vgzNTni8SUX7y/CZr7VlWeHefQ1U57wcv0ni0gyqfh6Vfn8/o+posrzY1//rXdQDUVBWnAK+jjaYM715Xy22V9SScbqj95TuqzrqmRGHkPp/zfNgxpN346u9Xsmr3UV76+vzo2JL1B3h+/cHofi7B7HW2y3zc0AEA3L1kM3cv2czPrj+VD59yAuBylUZj3Lw8t+4AU775VNJrMBTO5oPHmDpiULmXYTAYSoD5lATW7ztGVyjCixtixUuXbDjIe40tQPYZj0vWH+g2VmjTd4fHbzkrun3/0m38bY2VWBvR2Loaj1rFT0+dMKSgc333Y7N535jBQKzJ96zRVq9DJ6v0pBPqo/NHDa6Oe3zUVRqMEI4oP3thS9rzOec4beLQrNfotmYFwxq99q5QJLpdKB6xWgaNrMtPBMasgtb1OaL0wX84vdtct/i98uQT4u678v1j+MmnTuEL86cCsfhCRxjFzudBNX07tidX7mXHofY4q9sWu5j0taeN49tXvi+n1+H0SUO5+7o5fPvKE+PGt7gKVCdzlabCuEoLp6MrzHuNLQV/DhgMhsrECLcsKKT7QaFZng5nTRnGGSniVZy4uxbbGjZqcGHWpotmjuTksfVxYx871WpW4VjcBlR5GVlnCba5E+K/6J3nqa0rlNX5nOf3Q+/PvpOZuzVXtwzFImWahiLKR+eckHliChJj3ALBMB84cRTDBlWnexinT4p/PkWEj506Ltpvcrb92lw4Y0T8+XLItE3Wqup7H38//3j+lJzeryLC1XPGMqAq/jHJysm4e66mopjxoP2VdftaCYaVuTkIcIPB0Hswn5JAOENcVCzGLXcB5iQQFAPnOImrdeLuYjFuhZ8vcc0DqrxUeT1xPTJTzU1MlshEPj1B3VmMqQLdC6XQGnyJrtJs25FlK5wSXeK5JEMkCiuvR7J2U2dDXFZywg+fVNfn90o3t7shd97Yfggo3PJuMBgqEyPcIGNPzUKajRdaENeN80WdbL3Vfg8hO/apGBa+ZAkH1T5P1A2XLpje2XeEZCbyyd50u0q7CbcilMQIhS03byHPZWJyQrbW154o+JsorIodW5bMouf88Eklho2btDis3dvK+IYBebv4DQZDZWOSE8jsWiqkLESxXKVANOA8qXBzra0Y7qbuVjRvtH6YiGUdSXZusO4TiWWgZiIWvJ7981TlsmYl1kkrhqu0GHXFqlxWQWdd2Vjw8hdu8Zm26UgssFx04RbM3VVq3KSFo6qs3HWUk8fVZ55sMBhSE4lA20HobIFICMJBCHfFbjuPQscRez8IwQ4ItIJ/AFQPBhSGToL3fbjoSzPCjdiXTKpyDTFXT56u0hJaMxyctRXL3dTdiuaJCy53Zy0mihHLdeuJ1nzLRK6lQMAK5vd7JbnFrQiu0mIIt9gaLeGSrWUr3/IxuVnc4oVVsa1dyQo4ZxJuxktaOAePBWg82sHN508u91IMhsohEoYDay0hFuyArjbwVsHxA5Ywa9poi7IuCAWgtRGO7rL2c8Hjh4jLYDHrKiPcSsVeOwsxWTZeZzBMYjmDVLR2hmg+HmC4HXyuqhzrDBW9NlUgFMaTUO7BsVYUK0aoWzcEV6HfVK7RxLFsLW7r97UmPWcmvB5hyfoDTEsoe9DktBUogHysgMnweoRn1u7n2nnji+4qTfyh4bzPjgeSC+YjbbEPIfecXYfbi2Lt8oiV5Qzw1o7DBMMR/N5YNwfnuTRxbKVj1e6jAMwcPbjMKzEYSkzbIWhrgpY9cHw/NG2AtmY4ftCyhHUcgfbDEOqEcIbvhMHjoKYevH7rb+SJMONyGDIBaodZYx6fJczCAWusaiAMHGkJQK/PuvUPsCx1HYetx1TVleTSjXADXttmBfMmc7Hdt3QbM+1SGJm+UJ9ff4B5/36AHd+/ErBiTcCqU1YMzprSwAsbDjKiroZ9LR3RMffaQ2lKQeRC9zZW3uiXu3PfrDGDOXisiSF2TbfEx7uFW1eKDhLLNzfz9T+uBmBobW7FQjuDETYdOM7/+8OquPGlm5q4df60nI6VSCGZxG46gxG2NrUx/8cvZTzejFGD2HTgeMY6fKdPbmDhqr1MHh4vWIfYz9+LGw4mrZh//f2vR7df3tjEnPFW8PpbO44wfFDhhVo/fMoJPLlyLwAHWgM8sWI3nz5zYjdXaUOOr7Mhe5ZvaWZglZd5k0xGqaEPEOyA1r2WZWzHcmjZDfvfg0NbLbekG28V1I22BNig0ZboGjQK/DWWxW3ELBg+HXw11nGraqF+vCXAfOkz/XPC44GBw4t3vCQY4UZ8hmIirR3BbnWo3rvzg6za3cKAKi//8/JWnlvXvX4bwGHbwjF/1siirPPm86fwn89uYvLwWnY0t3H1nBP4wTUnA3DW5GHsPrynaB/Y3dpY+Tzdgst/dt2pbGs+zinjumevVfvjXaUdwXBS4bZuX0t0O9e4nBq/J+pmHTawil/9w+lcd99rORWQTUWxWjAltndKZ3197OazONYZSlk81+Hvz5zAhdNHMGFYbdz4qbYQS7TGOmzYfyzpuM8jnDm58J6WP/rEKdx47mSG1vq58EcvRS18ia7SL8yfxoUzRzB8UDVtgTDPrTsQLcBsyJ9QOMIrW5qZPbY+WjrGYKg42g/DwfVwbJ/lpgx3QeCYNR7usixarXvh4AZob3a5KwVqGyxr2PuvhYbJUDcGBo+FgSOsfU//SHAywo1YAVjnCyYUjg/cjjUbtz4M62r8nDfdUtQTGuK/PN04x3MKphaKiDBt5CA6gxECoTDjh9ZGRcpYu3L9uKGp15ML3bNKvd1cpfW1fk6dkFwoVvu8NB+Pmactq0v65yGTYElk9OAadhxqB+D94+qZM34IJ48bUqTkhPwziePWWF/DTnuN1vFSf7AMG1SdscYbWM9Tomhzxgdk0fYK4q3L4YhGOx8UQpXPw5zxQ+zODfHZtBC79hq/l9MmxiyCmw8mF5SG3Hhm7QG2NrXxlUtnlHsphv5G6144tMWKDwscs1yWoQ5rrLMFQl0QbLPEWFsT3YtaYQX0e6usv9phMPkCy3I16iTLijbhbEu4GYxwg/isP+s2PnDb3as0G5yWQ8X68ndT7fPQFgjFdUywxq0vxWJFDyVqqPgYt+wyI93lQIpRoiORuAQJ19pSxXjlQq6vebaUuqVTtT+7RvPuHymhAsueJOIkp7gLDydmIset2ZQBKQovbzrI4BofV+ZQyNpgSEkoYLkU2w9Z4ivYAU3roXmzFbx/7IAlzo7tt8VYEqrqoH6sHTtWBVMuguEzYOxcy405cHgsNsxf+I/H/oIRbnS3DCTWoMrVbRaKqJVNWEA2aiqqfV6OOoV2/W7hVmJBkJBVms18d2hfsYripj6fN3p76HiOmUBJKMVrZx2v9K9TNiI5WhQ4XCqB6o1aqp1yI6ksqqaxfOGoKss3N3PutOEm+cOQHZGwJcjamiw35fH9lqVs/2rY8Qoc3pr8cf5aGHyC9VfbAKNPgaETYfwZ4LfjxerG2GKstnvzZUPBGOGGy9KWYHlzxnK1nAVCdjZdCaw21X4PrYed7gUxUVHqGlhOHbfE86ab76ZYbahSn89pYO6pKFdpIoVmqWY8vi9LV6nzXi9SEkb3dcTe/53B9OVGTGP5wtnW3Mbelk5uvbi0QdGGXoaqVW/s4AbYsQxWPWZZzkIB6DqevNyFvxZOmAsnfxKq6yw3Ze1wS5DVj4eGKUaMlZl+L9xU1WVpixdwzliuX26BYJhB1b6SuUqTtZ3KlIlYjPO6xVHG+QlzSm1xc2e8FrWOW29zlWZ5/aniz4q2Dr8nWig6U4Ff4yotnOWbmwE4f9qIDDMNfYpI2K491mm5LA9vg3ULLVfm/vcs0eZm6sVQPw681Va2Zd0JMGAo1I2CAQ1WVqZT/sJQsfR74fb4W7txWpXuONTO7sPt/PjZjdH7X9zYxIsbm/BI9sHzv3hpKyeOGczTa/YDxXeVOj1AS+liSmzfasW45eYqdeOI37+t2c/ZU4dRP8DPoeMBfrV8e1HW67iHqn1e9hzp4O7nN/OVS6fnfbyY6O5drtIqn4en1+wnFI5Ee4+GI8pv39gZN2/hqr3c8eETOdDaWZJ1WZa/CD9bspnH39rN2CGp41eMxa1wlm1uZnzDgKRJK4ZeQCRsBfUfXG+5L1sbYePTVvmLSBg0DBqxaoRp2BprPxRf7NVhQAMMmwrTLoFh06zyF8NnWNsjZ/X8tRmKTr8Xbrf/6T0Aaqu8tHeFOf+HLyadl6oU24dPOYFfLd/O9WdM4LE3dwF0EyPF/GKaOboOVjnH9caNV3k9RSs9csZkK3tnfMMAAsEINUmyStOuc1Qdz6yNlUkJhMI0Hu3g8795m4tmjuDhG8/gyZV7OdBqZZ5eeXLuAdVfuGhqtAacU2vPyVb6yfObOG/6cE6bmF95lGK5EG+9aBrf+F9rjSIwPk0WcjFwxNqava3ROm3r97XyL0+ujc4ZN3QAe4508NdVe6M/RoYVoY6bm2qfh1V7jrL4vX0ANNpFrpMxpr6GKp+Hm86bUtQ1VBIicjlwN+AFHlDV7xfr2MFwhNe3HeLDp5xQrEMa8kUVmjdZIiwUsERY8yarrEWw06pHFuywLGTH7Sr+oQ4rtiwx03LgCJhwllX4VbxWqQvx2NseKwvTKRrr8VvB/nWjYPyZxa1LZqg4/n97Zx5kV1Xn8c+v+6W37BuZYDqSTEJiQIslkVCsE7YIaqbGwQlaRcSoNTrOoBQ1kJGpcmCmZrAsHCmdQVTGpQSVRcyAklEEq5jCkDASyWo6AUzHrASyQNLp5cwf99yX+16/3l7ft5zX30/Vq7733OX9zjm3f/f3zjm/32/EG24fuWAmD679A5+8ZDZffXp7zrGL50zhubZoCuKO695V8PpzWidkA+7etewsVj32Mg+/2J49fvMVc6lLcRrzb/5sDo/9Xzs7DryVY1ScO3MiW+9amtp3tU5q4dV/uw7nHM5BXZ0l1rgNbMzccvU8/vaKuWzdc5QPfO05Orp6OH4yGsWKw2Mc94vXt9y5lKYiRg+vX9jK9Qtb6e5x2RG3qxZM46EXdgFwdJCZGwqRRsorgA8vauXDiyIZofRZA25bOo+PfHMtb5/MjaEH8J2bFnH5vNM40dnN/H98irc7u7NhX/oK61IsjZm6bBxDgEvm9r32avKYRrbemd6zW22YWT3wdeAqoB1YZ2arnXOb07j/ulcPcayjq982FgPgnF+s/2ZkUB3bGxlarjvKU9nTHeWjPLQjMrbiUbD4b3dnNDW5b1O0n0+mOTKmWiZB04Roe/KcyPDKNESjZKOnRGvIxs+IpivHt2otmSjIiDfc4rVhhYK2jm8+Nc8/mGnSTH0doxtzm7S5If31O3FwzfyRr1K8+MwsGxok/r7BOkKMqq+juaHv/Jmx12HTqL49DgdD0hhKa2ozrZRXMeXy9Iuf4xzPaD962NIQPZvJgNPxlHippkpjBurfWjXaPO8F2pxzOwHM7IfAMmDYhtuRP27nyR8/yl+0dHNZTxdsiZ/XvNGbnLUPLqXyPNL6jmKu6TwRpTjq8n87jpxKCP72oWhUK55mdD0Jw6sn8qrsOtFbhoJY5C1Zl4lGv+rqT42GjW+FxZ+OEotPeGdkkI1qiaL1NyuThUiPEW+4xa+LQi+ucc2ZXucNRO9UUaX7xVTuMApxXfqKzF/4muhFcrKrh66e6EUeX93R3X+YiOHICEMP6JvkZEojbuUmlvdkV66DTfKYmdGQn9Ehde/ZuuwoI6QXXzBQ3gHsSuy3Axfkn2RmnwI+BTBz5sxB3XjDs4/yLx3/Gu38ZJhS1gJ1GZ9DcqoP5urzRY6e4qcZ6/KmHOujEbDGMX7BfrMPczEZmsb5/JSJqcrRU6NyISrIiDfcYuosyhZwMpE1oZiMB4VyfJaKchsVxXxfdnQn4Z0b09HZv7dhMSTb2/U3OjAAHV091FnpvXXTJq5/fixC6B33L2nQpWk8x/cUQ8M5dz9wP8DChQsH9fBe/MGV7HzlUmZPGe1LEv3Yq0/7OjaM8kFfU8z5Q5DLvFFVr1eaqH30lCdozOQZbs1FGG4FcnyWinIH2iwmVlzWkOjs6TVd2tHVk3pcs0wiOn9X9/AMt8ZMfeoGTak5NQ2aiEVYwEM2OZVZimdUIT5y2A20JvZn+LJhY2OmMvvdCgEixEhCP4sT5E89thSxPq3XiFsJpzOtzBNQxXxbXP8oA0Xuot2Oru7UjYaknTWceG4dnd1BRvRPtndMIQ/ZOMNCKYzn/O8SrAPmmtksM2sAlgOrKyyTECJQNOKWIH+UoJhRg3JOlVaKoYz0xV6Ldz+1lasXTAOiKO//8JOXee31t0v6gn9+50Gue890tuw5wse/s46508YyrinD3R96Ty8nkuw1O17nk99bz7GOLqaODc+lPn7e7nh8I62TWnhm635m+hAkOYbbqDra9h9lQ/vhfmOsFS1HgEZvqXDOdZnZZ4E1ROFAHnDObRrgMiGEKMiIN9w+u2QuB4518KHzZ3Dw2El+sXkf2/YdZdLoBq5ccBrfe34sW/ce5fqFMwZ1v0WzJnHW6ePY9McjAJw5bUzqMt97w7nc9+yOkty7Py478zQumbtv0G0Bud6C/7P5VFy3B9dGMe/OOj3dhb5zpo7JxuQ75gMVL7//Nxw+3smew1Gw2ZsumtVnfLeH1+/KJqmfMiZEw+2UwbTigRcAuPJdUWy/5MhaY6aeDe2HAbhozuTU5Vgy/zQ27j7CS7uiyO13LTs79e8ICefcz4CfVVoOIUT4jHjDberYRv7jo+cDcOs187j1mnk5x5/63KVDut/8PxnHk393CWfc/iRAdrQjTc6cNpZ7/uqc1O87EDMnt/D9lb2c4YZF2iNumfo6Nt+5lKvu+XV2veLJvCnT/P0kPQmHhr++LLyAsIXa80QfU6Uxty1NP5r6kvnTWDJ/Wur3FUKIkY7mM0pMaIvby02pppIbR9X18mKNB//6S8KedGcIcZq70PPW0dXdy0M2d9o0vHoKIcRIRYabqCilWguVHwA2LoP+nRaSo3GhhQLpi0IesrnTplIDQggRChXT2GZWb2a/NbMn/P4sM1trZm1m9iPvfYWZNfr9Nn/8jErJLNKnVEZDMk5ZtqyAx2U+w/FErVbe6ujqZSCfCqZcOwaqEEKMBCr5U/tmYEti/27gK865OcAbwEpfvhJ4w5d/xZ8nAmVMnjdnyaZK8zIDxGWQG+Msn/6mUUPlyImuPjN6hBirTgghRjIVMdzMbAZwHfAtv2/AEuARf8p3gT/328v8Pv74FaY3TbCMa8o33Eo4Vdo59KnS/GtqgSPHO/sMdaOwHUIIERaV0tr/Dvw9EL8lJwNvOue6/H47UX4/SOT588cP+/NFgIzNSyPWVKKF8Zl6Y9u+o7z7i2s47kfYYg/fOx7fyP+2Hex1zeO/3c36197I7jcXEYC5Guno6sHlJdCODbY4zp4QQogwsOHkcyzqC83eD1zrnPuMmV0O3Ap8DPiNnw7FzFqBnzvnzjazjcBS51y7P7YDuMA5dzDvvtkEzcDZwMZy1KeETAF6WxfhELr8EH4dQpcfhlaHdzrnaiL/k5kdAF4b5Omh9nOockO4skvu8lIS/VWJOG4XAR80s2uBJmAc8FVggpll/KhaMpdfnOev3cwywHjg9fybJhM0m9l659zCktekhIReh9Dlh/DrELr8UBt1KIahGKChtlGockO4skvu8lIqucs+T+KcW+Wcm+GcO4MoZ9+vnHMfBZ4B/tKftgL4qd9e7ffxx3/lyj1MKIQQQghRBVTTApfbgFvMrI1oDdu3ffm3gcm+/Bbg9grJJ4QQQghRUSqa8so59yzwrN/eCby3wDkngOuHeOv7hytbFRB6HUKXH8KvQ+jyQ23UodSE2kahyg3hyi65y0tJ5C67c4IQQgghhCiOapoqFUIIIYQQ/VBzhpuZLTWzbT5FVtWshzOzVjN7xsw2m9kmM7vZl08ys1+Y2Xb/d6IvNzO719fjd2Z2XuJeK/z5281sRV/fWaJ6DDtVmZmt8uXbzOyaMss/wcweMbOtZrbFzC4MqQ/M7PP++dloZg+ZWVO194GZPWBm+31on7gstTY3s/PN7GV/zb1mIydAd7Xpu9D1XIj6LVSdFoouq0r95ZyrmQ9QD+wAZgMNwAZgQaXl8rJNB87z22OB3wMLgC8Bt/vy24G7/fa1wM8BAxYDa335JGCn/zvRb08sYz1uAR4EnvD7PwaW++37gE/77c8A9/nt5cCP/PYC3y+NwCzfX/VllP+7wCf8dgMwIZQ+IApG/QrQnGj7j1V7HwCXAucBGxNlqbU58II/1/y17yvX81TJD1Wo7whczxGgfiNAnUZAuowq1F8V+wcv0cNwIbAmsb8KWFVpufqQ9afAVcA2YLovmw5s89vfAG5InL/NH78B+EaiPOe8Ess8A3iaKD3ZE/5BOwhk8tsfWANc6Lcz/jzL75PkeWWQf7xXFpZXHkQfcCqLyCTfpk8A14TQB8AZeYovlTb3x7YmynPOq+VPCPouJD0Xon4LVaeFpsuqTX/V2lRpNj2WJ5k6q2rww7znAmuBac65Pf7QXmCa3+6rLpWsYxqpyiop/yzgAPBffjrkW2Y2mkD6wDm3G/gy8AdgD1GbvkhYfRCTVpu/w2/nl48EqqEf+yRAPReifgtSp9WALquo/qo1w63qMbMxwKPA55xzR5LHXGRyu4oINgAWpSrb75x7sdKyDIMM0ZD3fzrnzgXeIi8uYJX3wURgGZGyPh0YDSytqFApUM1tLoojND0XsH4LUqfVki6rRPvWmuEWp8eKSabOqjhmNopImf3AOfeYL95nZtP98enAfl/eV10qVcc4VdmrwA+JphOyqcoKyJKV03JTlVWyj9qBdufcWr//CJHSC6UPrgRecc4dcM51Ao8R9UtIfRCTVpvv9tv55SOBaujHXgSq50LVb6HqtNB1WUX1V60ZbuuAud4zpYFoEePqCssERN4mRFkgtjjn7kkcSqb0WkFuqq8bvZfKYuCwH5pdA1xtZhP9r5arfVlJcemlKlsNLPdeQrOAuUSLM0uOc24vsMvM5vmiK4DNBNIHRNMKi82sxT9PsfzB9EGCVNrcHztiZot9m9yYuFetU3X6LlQ9F6p+C1inha7LKqu/0l7EV+kPkVfH74m8S75QaXkScl1MNJz6O+Al/7mWaJ7+aWA78Etgkj/fgK/7erwMLEzc6+NAm//cVIG6XM4pr6vZRP8obcDDQKMvb/L7bf747MT1X/D12kaZPQCBc4D1vh8eJ/LwCaYPgH8CtgIbge8TeVNVdR8ADxGtY+kkGiFYmWabAwt9e+wAvkbeQu1a/lSbvqsFPReafgtVp4Wiy6pRfylzghBCCCFEINTaVKkQQgghRM0iw00IIYQQIhBkuAkhhBBCBIIMNyGEEEKIQJDhJoQQQggRCDLcRNVjZpPN7CX/2WtmuxP7DXnnrjGzsQPcr93MJpRWaiGEiJAOE2micCAiKMzsi8Ax59yX88qN6HnuKXhh7rntwNnOuTdLI6UQQhRGOkwMF424iWAxszlmttnMfgBsAqYnf4ma2X+b2YtmtsnMPlFZaYUQIhfpMFEMmYFPEaKqmQ/c6JxbDxD9aG/JnrIAAADfSURBVM2ywjl3yMxagPVm9qhz7o1KCCmEEH0gHSaGhEbcROjsiBVeAT5vZhuA54mS9/5p+cQSQohBIR0mhoRG3ETovFWo0MyuBC4FFjvnjpvZc0T57oQQopqQDhNDQiNuolYZDxzyCu8sYFGlBRJCiCEgHSYKIsNN1CpPAi1mthn4Z2BtheURQoihIB0mCqJwIEIIIYQQgaARNyGEEEKIQJDhJoQQQggRCDLchBBCCCECQYabEEIIIUQgyHATQgghhAgEGW5CCCGEEIEgw00IIYQQIhBkuAkhhBBCBML/AxmKpIOwF1i0AAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 720x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "evaluate(rmpx6, encoder_bits=2, trials=10_000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 3 µs, sys: 1 µs, total: 4 µs\n",
-      "Wall time: 7.15 µs\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "# evaluate(rmpx6, encoder_bits=2, trials=15_000)"
-   ]
+   "source": []
   }
  ],
  "metadata": {
diff --git a/examples/racs/rmpx/racs_in_rmpx.py b/examples/racs/rmpx/racs_in_rmpx.py
index d0e13544..5d512fbe 100644
--- a/examples/racs/rmpx/racs_in_rmpx.py
+++ b/examples/racs/rmpx/racs_in_rmpx.py
@@ -7,7 +7,6 @@
 
 from lcs.agents.racs import Configuration, RACS
 from lcs.metrics import population_metrics
-from lcs.representations.RealValueEncoder import RealValueEncoder
 
 # Configure logger
 logging.basicConfig(level=logging.INFO)
@@ -30,21 +29,20 @@ def _rmpx_metrics(pop, env) -> Dict:
     rmpx = gym.make('real-multiplexer-3bit-v0')
 
     # Create agent
-    encoder = RealValueEncoder(resolution_bits=5)
     cfg = Configuration(rmpx.observation_space.shape[0],
                         rmpx.action_space.n,
-                        encoder=encoder,
                         user_metrics_collector_fcn=_rmpx_metrics,
                         epsilon=1.0,
                         do_ga=True,
                         theta_r=0.9,
                         theta_i=0.3,
-                        theta_ga=100,
+                        theta_ga=25,
+                        mutation_noise=0.5,
                         chi=0.5,
-                        mu=0.15)
+                        mu=1)
 
     agent = RACS(cfg)
-    population, metrics = agent.explore_exploit(rmpx, 100)
+    population, metrics = agent.explore(rmpx, 5000)
     logging.info("Done")
 
     # print reliable classifiers
@@ -55,5 +53,6 @@ def _rmpx_metrics(pop, env) -> Dict:
         logging.info(cl)
 
     # print metrics
+    logging.info("Printing metrics")
     for m in metrics:
         logging.info(m)
diff --git a/lcs/__init__.py b/lcs/__init__.py
index a74a5124..24f542fd 100644
--- a/lcs/__init__.py
+++ b/lcs/__init__.py
@@ -1,3 +1,16 @@
+import numpy as np
+
 from .utils import check_types
 from .Perception import Perception
 from .TypedList import TypedList
+
+# Tolerance for comparing two real numbers
+DELTA = 0.01
+
+
+def is_different(a: float, b: float):
+    return abs(a - b) > DELTA
+
+
+def clip(val: float):
+    return np.clip(val, 0, 1).tolist()
diff --git a/lcs/agents/PerceptionString.py b/lcs/agents/PerceptionString.py
index b5821b37..634365bb 100644
--- a/lcs/agents/PerceptionString.py
+++ b/lcs/agents/PerceptionString.py
@@ -16,8 +16,8 @@ def __init__(self, observation, wildcard='#', oktypes=(str,)):
     @classmethod
     def empty(cls,
               length: int,
-              wildcard: Any ='#',
-              oktypes: Tuple[Any]=(str,)):
+              wildcard: Any = '#',
+              oktypes: Tuple[Any] = (str,)):
         """
         Creates a perception string composed from wildcard symbols.
         Note that in case that wildcard is an object is get's copied
diff --git a/lcs/agents/racs/Classifier.py b/lcs/agents/racs/Classifier.py
index 20b60214..9d74240a 100644
--- a/lcs/agents/racs/Classifier.py
+++ b/lcs/agents/racs/Classifier.py
@@ -2,11 +2,13 @@
 
 import random
 from typing import Optional, List, Callable, Dict
+from copy import deepcopy
 
 import numpy as np
 
-from lcs import Perception
-from lcs.representations import UBR
+from lcs import Perception, is_different, clip
+from lcs.representations import Interval, FULL_INTERVAL
+
 from . import Condition, Effect, Mark, Configuration
 
 
@@ -61,11 +63,9 @@ def build_effect(initial):
         self.tga = tga
         self.tav = tav
 
-        # TODO: not used yet
-        self.ee = 0
+        self.ee = False
 
     def __eq__(self, other):
-        # TODO: here we should base on intervals somehow
         if self.condition == other.condition and \
                 self.action == other.action and \
                 self.effect == other.effect:
@@ -100,9 +100,9 @@ def copy_from(cls, old_cls: Classifier, time: int):
             copied classifier
         """
         new_cls = cls(
-            condition=Condition(old_cls.condition, old_cls.cfg),
+            condition=deepcopy(old_cls.condition),
             action=old_cls.action,
-            effect=old_cls.effect,
+            effect=deepcopy(old_cls.effect),
             quality=old_cls.q,
             reward=old_cls.r,
             immediate_reward=old_cls.ir,
@@ -163,8 +163,6 @@ def specialize(self,
             Requires the effect attribute to be a wildcard to specialize it.
             By default false
         """
-        p0_enc = list(map(self.cfg.encoder.encode, p0))
-        p1_enc = list(map(self.cfg.encoder.encode, p1))
 
         for idx, item in enumerate(p1):
             if leave_specialized:
@@ -172,14 +170,16 @@ def specialize(self,
                     # If we have a specialized attribute don't change it.
                     continue
 
-            if p0_enc[idx] != p1_enc[idx]:
+            if is_different(p0[idx], p1[idx]):
+                # For single step environments that is
+                # most often the validation bit case
                 noise = np.random.uniform(0, self.cfg.cover_noise)
-                self.condition[idx] = UBR(
-                    self.cfg.encoder.encode(p0[idx], -noise),
-                    self.cfg.encoder.encode(p0[idx], noise))
-                self.effect[idx] = UBR(
-                    self.cfg.encoder.encode(p1[idx], -noise),
-                    self.cfg.encoder.encode(p1[idx], noise))
+                self.condition[idx] = Interval(
+                    clip(p0[idx] - noise),
+                    clip(p0[idx] + noise))
+                self.effect[idx] = Interval(
+                    clip(p1[idx] - noise),
+                    clip(p1[idx] + noise))
 
     def is_reliable(self) -> bool:
         return self.q > self.cfg.theta_r
@@ -211,16 +211,16 @@ def does_anticipate_change(self) -> bool:
         return self.effect.specify_change
 
     def does_anticipate_correctly(self,
-                                  previous_situation: Perception,
-                                  situation: Perception) -> bool:
+                                  p0: Perception,
+                                  p1: Perception) -> bool:
         """
         Checks whether classifier correctly performs anticipation.
 
         Parameters
         ----------
-        previous_situation: Perception
+        p0: Perception
             Previously observed perception
-        situation: Perception
+        p1: Perception
             Current perception
 
         Returns
@@ -228,18 +228,15 @@ def does_anticipate_correctly(self,
         bool
             True if anticipation is correct, False otherwise
         """
-        p0_enc = list(map(self.cfg.encoder.encode, previous_situation))
-        p1_enc = list(map(self.cfg.encoder.encode, situation))
-
         for idx, eitem in enumerate(self.effect):
             if eitem == self.cfg.classifier_wildcard:
-                if p0_enc[idx] != p1_enc[idx]:
+                if is_different(p0[idx], p1[idx]):
                     return False
             else:
-                if p0_enc[idx] == p1_enc[idx]:
+                if not is_different(p0[idx], p1[idx]):
                     return False
 
-                if p1_enc[idx] not in eitem:
+                if p1[idx] not in eitem:
                     return False
 
         return True
@@ -258,7 +255,7 @@ def set_mark(self, perception: Perception) -> None:
             current situation
         """
         if self.mark.set_mark_using_condition(self.condition, perception):
-            self.ee = 0
+            self.ee = False
 
     def set_alp_timestamp(self, time: int) -> None:
         """
@@ -283,7 +280,7 @@ def is_more_general(self, other: Classifier) -> bool:
         Checks if the current classifier is more general than the other.
         The average area covered by condition attributes is compared.
 
-        For example UBR(0, 10) is more general than UBR(5, 6) because
+        For example interval [0, 10] is more general than [5, 6] because
         it covers more values.
 
         Parameters
@@ -316,25 +313,25 @@ def get_interval_proportions(self) -> Dict[int, int]:
         """
         counts = {1: 0, 2: 0, 3: 0, 4: 0}
 
-        r = self.cfg.encoder.range
+        fl = FULL_INTERVAL
 
         for i in self.condition:
-            if i.lower_bound != r[0] and i.upper_bound != r[1]:
+            if i.left != fl.left and i.right != fl.right:
                 counts[1] += 1
 
-            if i.lower_bound == r[0] and i.upper_bound != r[1]:
+            if i.left == fl.left and i.right != fl.right:
                 counts[2] += 1
 
-            if i.lower_bound != r[0] and i.upper_bound == r[1]:
+            if i.left != fl.left and i.right == fl.right:
                 counts[3] += 1
 
-            if i.lower_bound == r[0] and i.upper_bound == r[1]:
+            if i.left == fl.left and i.right == fl.right:
                 counts[4] += 1
 
         return counts
 
     def generalize_unchanging_condition_attribute(
-            self, randomfunc: Callable=random.choice) -> bool:
+            self, randomfunc: Callable = random.choice) -> bool:
         """
         Generalizes one randomly unchanging attribute in the condition.
         An unchanging attribute is one that is anticipated not to change
diff --git a/lcs/agents/racs/ClassifierList.py b/lcs/agents/racs/ClassifiersList.py
similarity index 88%
rename from lcs/agents/racs/ClassifierList.py
rename to lcs/agents/racs/ClassifiersList.py
index 0b199de2..33dbfb35 100644
--- a/lcs/agents/racs/ClassifierList.py
+++ b/lcs/agents/racs/ClassifiersList.py
@@ -14,18 +14,18 @@
 from . import Classifier
 
 
-class ClassifierList(TypedList):
+class ClassifiersList(TypedList):
 
     def __init__(self, *args) -> None:
         super().__init__((Classifier,), *args)
 
-    def form_match_set(self, situation: Perception) -> ClassifierList:
+    def form_match_set(self, situation: Perception) -> ClassifiersList:
         matching = [cl for cl in self if cl.condition.does_match(situation)]
-        return ClassifierList(*matching)
+        return ClassifiersList(*matching)
 
-    def form_action_set(self, action: int) -> ClassifierList:
+    def form_action_set(self, action: int) -> ClassifiersList:
         matching = [cl for cl in self if cl.action == action]
-        return ClassifierList(*matching)
+        return ClassifiersList(*matching)
 
     def expand(self) -> List[Classifier]:
         """
@@ -59,9 +59,9 @@ def get_maximum_fitness(self) -> float:
         return 0.0
 
     @staticmethod
-    def apply_alp(population: ClassifierList,
-                  match_set: ClassifierList,
-                  action_set: ClassifierList,
+    def apply_alp(population: ClassifiersList,
+                  match_set: ClassifiersList,
+                  action_set: ClassifiersList,
                   p0: Perception,
                   action: int,
                   p1: Perception,
@@ -69,7 +69,7 @@ def apply_alp(population: ClassifierList,
                   theta_exp: int,
                   cfg: Configuration) -> None:
 
-        new_list = ClassifierList()
+        new_list = ClassifiersList()
         new_cl: Optional[Classifier] = None
         was_expected_case = False
         delete_counter = 0
@@ -110,7 +110,7 @@ def apply_alp(population: ClassifierList,
             match_set.extend(new_matching)
 
     @staticmethod
-    def apply_reinforcement_learning(action_set: ClassifierList,
+    def apply_reinforcement_learning(action_set: ClassifiersList,
                                      reward: int,
                                      p: float,
                                      beta: float,
@@ -120,9 +120,9 @@ def apply_reinforcement_learning(action_set: ClassifierList,
 
     @staticmethod
     def apply_ga(time: int,
-                 population: ClassifierList,
-                 match_set: ClassifierList,
-                 action_set: ClassifierList,
+                 population: ClassifiersList,
+                 match_set: ClassifiersList,
+                 action_set: ClassifiersList,
                  p: Perception,
                  theta_ga: int,
                  mu: float,
@@ -147,6 +147,8 @@ def apply_ga(time: int,
 
             # Execute cross-over
             if random.random() < chi:
+                # TODO that will never happen for real values,
+                #  some tolerance is needed
                 if child1.effect == child2.effect:
                     crossover(child1, child2)
 
diff --git a/lcs/agents/racs/Condition.py b/lcs/agents/racs/Condition.py
index 848ac17c..417465f6 100644
--- a/lcs/agents/racs/Condition.py
+++ b/lcs/agents/racs/Condition.py
@@ -6,7 +6,7 @@
 from typing import Callable
 
 from lcs import Perception
-from lcs.representations.visualization import visualize
+from lcs.representations import FULL_INTERVAL
 from . import Configuration
 from .. import PerceptionString
 
@@ -17,6 +17,11 @@ def __init__(self, lst, cfg: Configuration) -> None:
         self.cfg = cfg
         super().__init__(lst, cfg.classifier_wildcard, cfg.oktypes)
 
+    # def __repr__(self):
+    #     return "|".join(visualize(
+    #         (ubr.left_bound, ubr.right_bound),
+    #         self.cfg.encoder.range) for ubr in self)
+
     @classmethod
     def generic(cls, cfg: Configuration):
         ps_str = [copy(cfg.classifier_wildcard) for _
@@ -46,8 +51,7 @@ def cover_ratio(self) -> float:
             0.0 means that condition is nothing is covered
             1.0 means that condition is maximally general
         """
-        maximum_span = self.cfg.encoder.range[1] + 1
-        return statistics.mean(r.bound_span / maximum_span for r in self)
+        return statistics.mean(r.span / FULL_INTERVAL.span for r in self)
 
     def specialize_with_condition(self, other: Condition) -> None:
         """
@@ -76,7 +80,7 @@ def generalize(self, idx: int) -> None:
         self[idx] = self.cfg.classifier_wildcard
 
     def generalize_specific_attribute_randomly(
-            self, func: Callable=random.choice) -> None:
+            self, func: Callable = random.choice) -> None:
         """
         Generalizes one randomly selected specified attribute.
 
@@ -89,17 +93,11 @@ def generalize_specific_attribute_randomly(
         specific_ids = [ci for ci, c in enumerate(self) if c != self.wildcard]
 
         if len(specific_ids) > 0:
-            ridx = func(specific_ids)
-            self.generalize(ridx)
+            r_idx = func(specific_ids)
+            self.generalize(r_idx)
 
     def does_match(self, perception: Perception):
-        encoded_perception = map(self.cfg.encoder.encode, perception)
-        return all(p in ubr for p, ubr in zip(encoded_perception, self))
+        return all(p in interval for p, interval in zip(perception, self))
 
     def subsumes(self, other: Condition):
-        return all(ci.incorporates(oi) for ci, oi in zip(self, other))
-
-    def __repr__(self):
-        return "|".join(visualize(
-            (ubr.lower_bound, ubr.upper_bound),
-            self.cfg.encoder.range) for ubr in self)
+        return all(oi in ci for ci, oi in zip(self, other))
diff --git a/lcs/agents/racs/Configuration.py b/lcs/agents/racs/Configuration.py
index 9487bade..3dc79fdb 100644
--- a/lcs/agents/racs/Configuration.py
+++ b/lcs/agents/racs/Configuration.py
@@ -1,14 +1,13 @@
 from typing import Callable
 
 from lcs.agents import EnvironmentAdapter
-from lcs.representations import UBR
+from lcs.representations import Interval, FULL_INTERVAL
 
 
 class Configuration:
     def __init__(self,
                  classifier_length: int,
                  number_of_possible_actions: int,
-                 encoder=None,
                  environment_adapter=EnvironmentAdapter,
                  user_metrics_collector_fcn: Callable=None,
                  metrics_trial_frequency: int = 5,
@@ -28,15 +27,11 @@ def __init__(self,
                  mu: float=0.3,
                  chi: float=0.8) -> None:
 
-        if encoder is None:
-            raise TypeError('Real number encoder should be passed')
-
-        self.oktypes = (UBR,)
-        self.encoder = encoder
+        self.oktypes = (Interval,)
 
         self.classifier_length = classifier_length
         self.number_of_possible_actions = number_of_possible_actions
-        self.classifier_wildcard = UBR(*self.encoder.range)
+        self.classifier_wildcard = FULL_INTERVAL
 
         self.environment_adapter = environment_adapter
 
diff --git a/lcs/agents/racs/Effect.py b/lcs/agents/racs/Effect.py
index ed02eb5f..743ebc40 100644
--- a/lcs/agents/racs/Effect.py
+++ b/lcs/agents/racs/Effect.py
@@ -2,8 +2,7 @@
 
 from copy import copy
 
-from lcs import Perception
-from lcs.representations.visualization import visualize
+from lcs import Perception, is_different
 from . import Configuration
 from .. import PerceptionString
 
@@ -18,10 +17,10 @@ def __init__(self, lst, cfg: Configuration) -> None:
         self.cfg = cfg
         super().__init__(lst, cfg.classifier_wildcard, cfg.oktypes)
 
-    def __repr__(self):
-        return "|".join(visualize(
-            (ubr.lower_bound, ubr.upper_bound),
-            self.cfg.encoder.range) for ubr in self)
+    # def __repr__(self):
+    #     return "|".join(visualize(
+    #         (interval.left_bound, interval.right_bound),
+    #         self.cfg.encoder.range) for interval in self)
 
     @property
     def specify_change(self) -> bool:
@@ -72,15 +71,13 @@ def is_specializable(self, p0: Perception, p1: Perception) -> bool:
         bool
             True if specializable, false otherwise
         """
-        encoded_p0 = list(map(self.cfg.encoder.encode, p0))
-        encoded_p1 = list(map(self.cfg.encoder.encode, p1))
 
-        for p0i, p1i, ei in zip(encoded_p0, encoded_p1, self):
+        for p0i, p1i, ei in zip(p0, p1, self):
             if ei != self.wildcard:
-                if p1i not in ei or p0i == p1i:
+                if p1i not in ei or not is_different(p0i, p1i):
                     return False
 
         return True
 
     def subsumes(self, other: Effect) -> bool:
-        return all(ei.incorporates(oi) for ei, oi in zip(self, other))
+        return all(oi in ei for ei, oi in zip(self, other))
diff --git a/lcs/agents/racs/Mark.py b/lcs/agents/racs/Mark.py
index 843ed63d..cc92627f 100644
--- a/lcs/agents/racs/Mark.py
+++ b/lcs/agents/racs/Mark.py
@@ -1,9 +1,9 @@
 import random
 from typing import List
 
-from lcs import Perception, TypedList
+from lcs import Perception, TypedList, is_different
 from lcs.agents.racs import Configuration, Condition
-from lcs.representations import UBR
+from lcs.representations import Interval
 
 
 class Mark(TypedList):
@@ -22,15 +22,14 @@ def is_marked(self) -> bool:
         """
         return any(len(attrib) != 0 for attrib in self)
 
-    def complement_marks(self, perception: Perception) -> bool:
+    def complement_marks(self, p: Perception) -> bool:
         """
         Adds current perception to the corresponding marking attributes.
         Only extends already marked attributes.
-        Perception get's encoded before being stored.
 
         Parameters
         ----------
-        perception: Perception
+        p: Perception
             agent's perception
 
         Returns
@@ -40,11 +39,11 @@ def complement_marks(self, perception: Perception) -> bool:
 
         """
         changed = False
-        encoded_perception = list(map(self.cfg.encoder.encode, perception))
 
-        for idx, attrib in enumerate(self):
-            new_elem = encoded_perception[idx]
-            if len(attrib) > 0 and new_elem not in self[idx]:
+        for idx, mark in enumerate(self):
+            new_elem = p[idx]
+
+            if len(mark) > 0 and self._not_in_mark(new_elem, self[idx]):
                 self[idx].add(new_elem)
                 changed = True
 
@@ -52,7 +51,7 @@ def complement_marks(self, perception: Perception) -> bool:
 
     def set_mark_using_condition(self,
                                  condition: Condition,
-                                 perception: Perception) -> bool:
+                                 p: Perception) -> bool:
         """
         Set's the mark using classifier condition.
         If it is already marked, it gets complemented using perception only.
@@ -63,7 +62,7 @@ def set_mark_using_condition(self,
         ----------
         condition: Condition
             classifier condition
-        perception: Perception
+        p: Perception
             agent's perception
 
         Returns
@@ -72,19 +71,18 @@ def set_mark_using_condition(self,
             True if any attribute was marked, False otherwise
         """
         if self.is_marked():
-            return self.complement_marks(perception)
+            return self.complement_marks(p)
 
         changed = False
-        encoded_perception = list(map(self.cfg.encoder.encode, perception))
 
         for idx, item in enumerate(condition):
             if item == self.cfg.classifier_wildcard:
-                self[idx].add(encoded_perception[idx])
+                self[idx].add(p[idx])
                 changed = True
 
         return changed
 
-    def get_differences(self, p0: Perception) -> Condition:
+    def get_differences(self, p: Perception) -> Condition:
         """
         Difference determination is run when the classifier anticipated the
         change correctly.
@@ -103,7 +101,7 @@ def get_differences(self, p0: Perception) -> Condition:
 
         Parameters
         ----------
-        p0: Perception
+        p: Perception
 
         Returns
         -------
@@ -115,27 +113,32 @@ def get_differences(self, p0: Perception) -> Condition:
         diff = Condition.generic(self.cfg)
 
         if self.is_marked():
-            enc_p0 = list(map(self.cfg.encoder.encode, p0))
 
             # Unique and fuzzy difference counts
             nr1, nr2 = 0, 0
 
             # Count difference types
             for idx, item in enumerate(self):
-                if len(item) > 0 and enc_p0[idx] not in item:
+                if len(item) > 0 and self._not_in_mark(p[idx], item):
                     nr1 += 1
                 elif len(item) > 1:
                     nr2 += 1
 
             if nr1 > 0:
-                possible_idx = [pi for pi, p in enumerate(enc_p0) if
-                                p not in self[pi] and len(self[pi]) > 0]
+                possible_idx = [p_idx for p_idx, p in enumerate(p) if
+                                self._not_in_mark(p, self[p_idx]) and
+                                len(self[p_idx]) > 0]
+
                 rand_idx = random.choice(possible_idx)
-                p = enc_p0[rand_idx]
-                diff[rand_idx] = UBR(p, p)
+                diff[rand_idx] = Interval(p[rand_idx], p[rand_idx])
             elif nr2 > 0:
-                for pi, p in enumerate(enc_p0):
+                for pi, p_val in enumerate(p):
                     if len(self[pi]) > 1:
-                        diff[pi] = UBR(p, p)
+                        diff[pi] = Interval(p_val, p_val)
 
         return diff
+
+    @staticmethod
+    def _not_in_mark(val: float, mark):
+        assert type(val) is float
+        return all(is_different(val, attrib) for attrib in mark)
diff --git a/lcs/agents/racs/RACS.py b/lcs/agents/racs/RACS.py
index bb4693fd..180de560 100644
--- a/lcs/agents/racs/RACS.py
+++ b/lcs/agents/racs/RACS.py
@@ -5,7 +5,7 @@
 from lcs.agents.Agent import TrialMetrics
 from lcs.strategies.action_selection import choose_action
 from ...agents import Agent
-from ...agents.racs import Configuration, ClassifierList
+from ...agents.racs import Configuration, ClassifiersList
 
 logger = logging.getLogger(__name__)
 
@@ -15,9 +15,9 @@ class RACS(Agent):
 
     def __init__(self,
                  cfg: Configuration,
-                 population: ClassifierList=None) -> None:
+                 population: ClassifiersList = None) -> None:
         self.cfg = cfg
-        self.population = population or ClassifierList()
+        self.population = population or ClassifiersList()
 
     def get_population(self):
         return self.population
@@ -50,7 +50,7 @@ def _run_trial_explore(self, env, time, current_trial=None) \
         action = env.action_space.sample()
         reward = 0
         prev_state = Perception.empty()
-        action_set = ClassifierList()
+        action_set = ClassifiersList()
         done = False
 
         while not done:
@@ -58,7 +58,7 @@ def _run_trial_explore(self, env, time, current_trial=None) \
 
             if steps > 0:
                 # Apply learning in the last action set
-                ClassifierList.apply_alp(
+                ClassifiersList.apply_alp(
                     self.population,
                     match_set,
                     action_set,
@@ -68,14 +68,14 @@ def _run_trial_explore(self, env, time, current_trial=None) \
                     time + steps,
                     self.cfg.theta_exp,
                     self.cfg)
-                ClassifierList.apply_reinforcement_learning(
+                ClassifiersList.apply_reinforcement_learning(
                     action_set,
                     reward,
                     match_set.get_maximum_fitness(),
                     self.cfg.beta,
                     self.cfg.gamma)
                 if self.cfg.do_ga:
-                    ClassifierList.apply_ga(
+                    ClassifiersList.apply_ga(
                         time + steps,
                         self.population,
                         match_set,
@@ -101,9 +101,9 @@ def _run_trial_explore(self, env, time, current_trial=None) \
             state = self.cfg.environment_adapter.to_genotype(raw_state)
 
             if done:
-                ClassifierList.apply_alp(
+                ClassifiersList.apply_alp(
                     self.population,
-                    ClassifierList(),
+                    ClassifiersList(),
                     action_set,
                     prev_state,
                     action,
@@ -111,14 +111,14 @@ def _run_trial_explore(self, env, time, current_trial=None) \
                     time + steps,
                     self.cfg.theta_exp,
                     self.cfg)
-                ClassifierList.apply_reinforcement_learning(
+                ClassifiersList.apply_reinforcement_learning(
                     action_set,
                     reward,
                     0,
                     self.cfg.beta,
                     self.cfg.gamma)
                 if self.cfg.do_ga:
-                    ClassifierList.apply_ga(
+                    ClassifiersList.apply_ga(
                         time + steps,
                         self.population,
                         match_set,
@@ -143,14 +143,14 @@ def _run_trial_exploit(self, env, time=None, current_trial=None) \
         state = self.cfg.environment_adapter.to_genotype(raw_state)
 
         reward = 0
-        action_set = ClassifierList()
+        action_set = ClassifiersList()
         done = False
 
         while not done:
             match_set = self.population.form_match_set(state)
 
             if steps > 0:
-                ClassifierList.apply_reinforcement_learning(
+                ClassifiersList.apply_reinforcement_learning(
                     action_set,
                     reward,
                     match_set.get_maximum_fitness(),
@@ -169,7 +169,7 @@ def _run_trial_exploit(self, env, time=None, current_trial=None) \
             state = self.cfg.environment_adapter.to_genotype(raw_state)
 
             if done:
-                ClassifierList.apply_reinforcement_learning(
+                ClassifiersList.apply_reinforcement_learning(
                     action_set,
                     reward,
                     0,
diff --git a/lcs/agents/racs/__init__.py b/lcs/agents/racs/__init__.py
index 910a4294..7dc7010b 100644
--- a/lcs/agents/racs/__init__.py
+++ b/lcs/agents/racs/__init__.py
@@ -3,5 +3,5 @@
 from .Effect import Effect
 from .Mark import Mark
 from .Classifier import Classifier
-from .ClassifierList import ClassifierList
+from .ClassifiersList import ClassifiersList
 from .RACS import RACS
diff --git a/lcs/agents/racs/components/alp.py b/lcs/agents/racs/components/alp.py
index 01cdf192..e8889607 100644
--- a/lcs/agents/racs/components/alp.py
+++ b/lcs/agents/racs/components/alp.py
@@ -77,6 +77,7 @@ def expected_case(cl: Classifier,
             diff.generalize_specific_attribute_randomly()
             no_spec_new -= 1
 
+    # TODO: here very specific intervals are inserted [p, p)
     child.condition.specialize_with_condition(diff)
 
     if child.q < 0.5:
@@ -121,6 +122,7 @@ def unexpected_case(cl: Classifier,
         return None
 
     child = cl.copy_from(cl, time)
+    # TODO: maybe here we can also create some interval (instead of generics)
     child.specialize(p0, p1, leave_specialized=True)
 
     if child.q < .5:
diff --git a/lcs/agents/racs/components/genetic_algorithm.py b/lcs/agents/racs/components/genetic_algorithm.py
index 56ac7987..21bfc2d9 100644
--- a/lcs/agents/racs/components/genetic_algorithm.py
+++ b/lcs/agents/racs/components/genetic_algorithm.py
@@ -4,10 +4,10 @@
 
 import numpy as np
 
+from lcs import clip
 from lcs.agents import PerceptionString
 from lcs.agents.racs import Classifier, Condition, Effect
-from lcs.representations import UBR
-from lcs.representations.RealValueEncoder import RealValueEncoder
+from lcs.representations import Interval
 
 logger = logging.getLogger(__name__)
 
@@ -24,14 +24,25 @@ def mutate(cl: Classifier, mu: float) -> None:
     mu: float
         probability of executing mutation on single interval bound
     """
-    encoder = cl.cfg.encoder
     noise_max = cl.cfg.mutation_noise
     wildcard = cl.cfg.classifier_wildcard
 
     for c, e in zip(cl.condition, cl.effect):
         if c != wildcard and e != wildcard:
-            _widen_attribute(c, encoder, noise_max, mu)
-            _widen_attribute(e, encoder, noise_max, mu)
+            if np.random.random() < mu:
+                noise = np.random.uniform(0, noise_max)
+                if np.random.rand() < .5:
+                    c.add_to_left(noise)
+                    e.add_to_left(noise)
+                else:
+                    c.add_to_right(noise)
+                    e.add_to_right(noise)
+
+                # restrict range to [0, 1]
+                c.p, c.q = clip(c.p), clip(c.q)
+                e.p, e.q = clip(e.p), clip(e.q)
+
+        # assert c == e
 
 
 def crossover(parent: Classifier, donor: Classifier):
@@ -68,23 +79,7 @@ def crossover(parent: Classifier, donor: Classifier):
     donor.effect = Effect(_unflatten(d_effect_flat), cfg=parent.cfg)
 
 
-def _widen_attribute(ubr: UBR, encoder: RealValueEncoder,
-                     noise_max: float, mu: float):
-
-    # TODO: we should modify both condition and effect parts with the
-    # same noise.
-    if np.random.random() < mu:
-        noise = np.random.uniform(-noise_max, noise_max)
-        x1p = encoder.decode(ubr.x1)
-        ubr.x1 = encoder.encode(x1p, noise)
-
-    if np.random.random() < mu:
-        noise = np.random.uniform(-noise_max, noise_max)
-        x2p = encoder.decode(ubr.x2)
-        ubr.x2 = encoder.encode(x2p, noise)
-
-
-def _flatten(ps: PerceptionString) -> List[int]:
+def _flatten(ps: PerceptionString) -> List:
     """
     Flattens the perception string interval predicate into a flat list
 
@@ -94,23 +89,24 @@ def _flatten(ps: PerceptionString) -> List[int]:
         list of all alleles (encoded)
     """
     return list(itertools.chain.from_iterable(
-        map(lambda ip: (ip.x1, ip.x2), ps)))
+        map(lambda ip: (ip.p, ip.q), ps)))
 
 
-def _unflatten(flatten: List[int]) -> List[UBR]:
+def _unflatten(flatten: List) -> List[Interval]:
     """
-    Unflattens list by creating pairs of UBR using consecutive list items
+    Unflattens list by creating pairs of Intervals using consecutive list items
 
     Parameters
     ----------
-    flatten: List[int]
-        Flat list of encoded perceptions
+    flatten: List[float]
+        Flat list of perceptions
 
     Returns
     -------
-    List[UBR]
-        List of created UBRs
+    List[Interval]
+        List of created Intervals
     """
     # Make sure we are not left with any outliers
     assert len(flatten) % 2 == 0
-    return [UBR(flatten[i], flatten[i + 1]) for i in range(0, len(flatten), 2)]
+    return [Interval(flatten[i], flatten[i + 1])
+            for i in range(0, len(flatten), 2)]
diff --git a/lcs/representations/Interval.py b/lcs/representations/Interval.py
new file mode 100644
index 00000000..4a76d166
--- /dev/null
+++ b/lcs/representations/Interval.py
@@ -0,0 +1,79 @@
+from __future__ import annotations
+
+from lcs import DELTA
+
+
+def _assert_proper_value(val: float):
+    assert type(val) is float
+    if val < 0.0 or val > 1.0:
+        raise ValueError(f'Value {val:.2f} not in range [0, 1]')
+
+
+class Interval:
+    """
+    Representation of interval for real-valued boundaries. Can work
+    for both OBR (ordered-bounded representation) and UBR (unordered-bounded
+    representation).
+
+    An interval is half-opened from the right side - [p, q)
+    """
+
+    __slots__ = ['p', 'q']
+
+    def __init__(self, p: float, q: float) -> None:
+        _assert_proper_value(p)
+        _assert_proper_value(q)
+
+        self.p = p
+        self.q = q
+
+    @property
+    def left(self) -> float:
+        return min(self.p, self.q)
+
+    @property
+    def right(self) -> float:
+        return max(self.p, self.q)
+
+    @property
+    def span(self) -> float:
+        return self.right - self.left
+
+    def add_to_left(self, val) -> Interval:
+        if self.p < self.q:
+            self.p -= val
+        else:
+            self.q -= val
+
+        return self
+
+    def add_to_right(self, val) -> Interval:
+        if self.p < self.q:
+            self.q += val
+        else:
+            self.p += val
+
+        return self
+
+    def __contains__(self, item):
+        assert type(item) in [Interval, float]
+
+        if type(item) is Interval:
+            return self.left <= item.left and self.right >= item.right
+
+        elif type(item) is float:
+            return self.left <= item < self.right
+
+        else:
+            return False
+
+    def __hash__(self):
+        return hash((self.left, self.right))
+
+    def __eq__(self, o) -> bool:
+        delta_left = abs(self.left - o.left)
+        delta_right = abs(self.right - o.right)
+        return (delta_left + delta_right) < DELTA * 2
+
+    def __repr__(self):
+        return f"[{self.left:.2f};{self.right:.2f}]"
diff --git a/lcs/representations/RealValueEncoder.py b/lcs/representations/RealValueEncoder.py
deleted file mode 100644
index 9b4660cb..00000000
--- a/lcs/representations/RealValueEncoder.py
+++ /dev/null
@@ -1,98 +0,0 @@
-from typing import Tuple
-
-import numpy as np
-
-
-class RealValueEncoder:
-    r"""
-    Real-value encoder.
-
-    Assumes that provided values are already in range [0,1].
-
-    This min-max normalization can be done as a prior step using the following
-    formula:
-
-    .. math::
-        x\prime = \frac{x - \textrm{min}(x)} {\textrm{max}(x)-\textrm{min}(x)}
-    """
-    def __init__(self, resolution_bits: int) -> None:
-        resolution = pow(2, resolution_bits)
-        step = 1 / resolution
-        self.upper_max = resolution - 1
-        self.splits = [x * step for x in range(0, resolution + 1)]
-
-    @property
-    def range(self) -> Tuple[int, int]:
-        """
-        Range the range (min,max) of available encoded values.
-
-        Returns
-        -------
-        Tuple[int, int]
-            Min, max values
-        """
-        return 0, self.upper_max
-
-    def encode(self, val: float, noise: float = 0.0) -> int:
-        """
-        Encodes the float value into `[0, 2^bits]` states.
-        This results in `2^bits + 1` different states.
-
-        Parameters
-        ----------
-        val : float
-            real-valued number in range [0,1]
-        noise: float
-            noise that is appended to the perception
-
-        Returns
-        -------
-        int
-            discrete state within resolution
-        """
-        if val < 0 or val > 1:
-            raise ValueError("Value not in correct [0, 1] range")
-
-        # Disturb value and limit it within range
-        val = np.clip(val + noise, 0, 1)
-
-        # Check boundary conditions
-        if val == 0:
-            return self.range[0]
-
-        if val == 1:
-            return self.range[1]
-
-        # In other cases iterate over splits to find correct bucket
-        bucket = -1
-        for i, _ in enumerate(self.splits[:-1]):
-            x1 = self.splits[i]
-            x2 = self.splits[i + 1]
-
-            if x1 <= val < x2:
-                bucket = i
-
-        assert bucket != -1
-
-        return bucket
-
-    def decode(self, encoded_val: int) -> float:
-        """
-        Decodes a discrete value to real-valued representation (still [0,1]
-        range)
-
-        Parameters
-        ----------
-        encoded_val : int
-            encoded value
-
-        Returns
-        -------
-        float
-            real-valued number from [0,1] range
-
-        """
-        if encoded_val < 0 or encoded_val > self.upper_max:
-            raise ValueError("Value is not from possible resolution range")
-
-        return encoded_val / self.upper_max
diff --git a/lcs/representations/UBR.py b/lcs/representations/UBR.py
deleted file mode 100644
index e25277cb..00000000
--- a/lcs/representations/UBR.py
+++ /dev/null
@@ -1,54 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass
-
-from dataslots import with_slots
-
-
-@with_slots
-@dataclass
-class UBR:
-    """
-    Real-value representation for unordered-bounded values.
-    """
-    x1: int
-    x2: int
-
-    @property
-    def lower_bound(self) -> int:
-        return min(self.x1, self.x2)
-
-    @property
-    def upper_bound(self) -> int:
-        return max(self.x1, self.x2)
-
-    @property
-    def bound_span(self) -> int:
-        return sum(1 for _ in range(self.lower_bound, self.upper_bound + 1))
-
-    def incorporates(self, other: UBR) -> bool:
-        """
-        Checks whether current UBR incorporates other.
-
-        Parameters
-        ----------
-        other: UBR
-            Other real-value representation
-
-        Returns
-        -------
-        bool
-            True if `other` is contained in this UBR, False otherwise
-        """
-        return self.lower_bound <= other.lower_bound and \
-            self.upper_bound >= other.upper_bound
-
-    def __contains__(self, item):
-        return self.lower_bound <= item <= self.upper_bound
-
-    def __hash__(self):
-        return hash((self.lower_bound, self.upper_bound))
-
-    def __eq__(self, o) -> bool:
-        return self.lower_bound == o.lower_bound \
-            and self.upper_bound == o.upper_bound
diff --git a/lcs/representations/__init__.py b/lcs/representations/__init__.py
index 53ddc3eb..d5ca4ddb 100644
--- a/lcs/representations/__init__.py
+++ b/lcs/representations/__init__.py
@@ -1 +1,3 @@
-from .UBR import UBR
+from .Interval import Interval
+
+FULL_INTERVAL = Interval(0., 1.)
diff --git a/lcs/utils.py b/lcs/utils.py
index 40960df4..c70605e3 100644
--- a/lcs/utils.py
+++ b/lcs/utils.py
@@ -1,3 +1,3 @@
 def check_types(oktypes, o):
-    if not isinstance(o, oktypes):
+    if type(o) not in oktypes:
         raise TypeError(f"Wrong element type: object {o}, type {type(o)}")
diff --git a/requirements.txt b/requirements.txt
index 6948e249..1d065b5a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,5 @@
 numpy>=1.8.2
 scipy>=1.1.0
-dataslots
 
 twine
 mypy==0.620
diff --git a/tests/lcs/agents/racs/components/test_alp.py b/tests/lcs/agents/racs/components/test_alp.py
index c6e39d14..391687aa 100644
--- a/tests/lcs/agents/racs/components/test_alp.py
+++ b/tests/lcs/agents/racs/components/test_alp.py
@@ -6,8 +6,7 @@
 from lcs.agents.racs import Configuration, Condition, Effect, Classifier
 from lcs.agents.racs.components.alp \
     import cover, expected_case, unexpected_case
-from lcs.representations import UBR
-from lcs.representations.RealValueEncoder import RealValueEncoder
+from lcs.representations import Interval
 
 
 class TestALP:
@@ -15,8 +14,7 @@ class TestALP:
     @pytest.fixture
     def cfg(self):
         return Configuration(classifier_length=2,
-                             number_of_possible_actions=2,
-                             encoder=RealValueEncoder(4))
+                             number_of_possible_actions=2)
 
     def test_should_handle_expected_case_1(self, cfg):
         # given
@@ -38,8 +36,8 @@ def test_should_handle_expected_case_2(self, cfg):
         p0 = Perception([.5, .5], oktypes=(float,))
         q = random.random()
         cl = Classifier(quality=q, cfg=cfg)
-        cl.mark[0].add(8)
-        cl.mark[1].add(8)
+        cl.mark[0].add(.505)
+        cl.mark[1].add(.505)
         time = random.randint(0, 1000)
 
         # when
@@ -55,7 +53,7 @@ def test_should_handle_expected_case_3(self, cfg):
         p0 = Perception([.5, .5], oktypes=(float,))
         q = 0.4
         cl = Classifier(quality=q, cfg=cfg)
-        cl.mark[0].add(2)
+        cl.mark[0].add(.2)
         time = random.randint(0, 1000)
 
         # when
@@ -63,7 +61,8 @@ def test_should_handle_expected_case_3(self, cfg):
 
         # then
         assert child is not None
-        assert child.condition == Condition([UBR(8, 8), UBR(0, 15)], cfg)
+        assert child.condition == Condition(
+            [Interval(.5, .5), Interval(0., 1.)], cfg)
         assert child.q == 0.5
 
     def test_should_handle_unexpected_case_1(self, cfg):
@@ -71,7 +70,7 @@ def test_should_handle_unexpected_case_1(self, cfg):
         p0 = Perception([.5, .5], oktypes=(float,))
         p1 = Perception([.5, .5], oktypes=(float,))
         # Effect is all pass-through. Can be specialized.
-        effect = Effect([UBR(0, 15), UBR(0, 15)], cfg=cfg)
+        effect = Effect([Interval(0., 1.), Interval(0., 1.)], cfg=cfg)
         quality = .4
         time = random.randint(0, 1000)
         cl = Classifier(effect=effect, quality=quality, cfg=cfg)
@@ -87,15 +86,17 @@ def test_should_handle_unexpected_case_1(self, cfg):
         assert child.talp == time
         # There is no change in perception so the child condition
         # and effect should stay the same.
-        assert child.condition == Condition([UBR(0, 15), UBR(0, 15)], cfg=cfg)
-        assert child.effect == Effect([UBR(0, 15), UBR(0, 15)], cfg=cfg)
+        assert child.condition == Condition(
+            [Interval(0., 1.), Interval(0., 1.)], cfg=cfg)
+        assert child.effect == Effect(
+            [Interval(0., 1.), Interval(0., 1.)], cfg=cfg)
 
     def test_should_handle_unexpected_case_2(self, cfg):
         # given
         p0 = Perception([.5, .5], oktypes=(float,))
         p1 = Perception([.5, .5], oktypes=(float,))
         # Effect is not specializable
-        effect = Effect([UBR(0, 15), UBR(2, 4)], cfg=cfg)
+        effect = Effect([Interval(0., 1.), Interval(.2, .4)], cfg=cfg)
         quality = random.random()
         time = random.randint(0, 1000)
         cl = Classifier(effect=effect, quality=quality, cfg=cfg)
@@ -114,7 +115,7 @@ def test_should_handle_unexpected_case_3(self, cfg):
         p0 = Perception([.5, .5], oktypes=(float,))
         p1 = Perception([.5, .8], oktypes=(float,))
         # Second effect attribute is specializable
-        effect = Effect([UBR(0, 15), UBR(10, 14)], cfg=cfg)
+        effect = Effect([Interval(0., 1.), Interval(.8, .9)], cfg=cfg)
         quality = 0.4
         time = random.randint(0, 1000)
         cl = Classifier(effect=effect, quality=quality, cfg=cfg)
@@ -129,14 +130,18 @@ def test_should_handle_unexpected_case_3(self, cfg):
         assert child is not None
         assert child.is_marked() is False
         assert child.q == .5
-        assert child.condition == Condition([UBR(0, 15), UBR(0, 15)], cfg=cfg)
-        assert child.effect == Effect([UBR(0, 15), UBR(10, 14)], cfg=cfg)
+        assert child.condition == Condition(
+            [Interval(0., 1.), Interval(0., 1.)], cfg=cfg)
+        assert child.effect == Effect(
+            [Interval(0., 1.), Interval(.8, .9)], cfg=cfg)
 
     @pytest.mark.parametrize("_p0, _p1, _child_cond, _child_effect", [
         ([.5, .5], [.5, .5],
-         [UBR(0, 15), UBR(0, 15)], [UBR(0, 15), UBR(0, 15)]),
+         [Interval(0., 1.), Interval(0., 1.)],
+         [Interval(0., 1.), Interval(0., 1.)]),
         ([.4, .5], [.9, .5],
-         [UBR(6, 6), UBR(0, 15)], [UBR(14, 14), UBR(0, 15)]),
+         [Interval(.4, .4), Interval(0., 1.)],
+         [Interval(.9, .9), Interval(0., 1.)]),
     ])
     def test_should_create_new_classifier_with_covering(
             self, _p0, _p1, _child_cond, _child_effect, cfg):
@@ -163,5 +168,5 @@ def test_should_create_new_classifier_with_covering(
         assert new_cl.tav == 0
         assert new_cl.tga == time
         assert new_cl.talp == time
-        # assert new_cl.num == 1
+        assert new_cl.num == 1
         assert new_cl.exp == 0
diff --git a/tests/lcs/agents/racs/components/test_genetic_algorithm.py b/tests/lcs/agents/racs/components/test_genetic_algorithm.py
index e5a57d0d..9d9ea9e2 100644
--- a/tests/lcs/agents/racs/components/test_genetic_algorithm.py
+++ b/tests/lcs/agents/racs/components/test_genetic_algorithm.py
@@ -6,8 +6,7 @@
 from lcs.agents.racs import Classifier, Configuration, Condition, Effect
 from lcs.agents.racs.components.genetic_algorithm import mutate, crossover, \
     _flatten, _unflatten
-from lcs.representations import UBR
-from lcs.representations.RealValueEncoder import RealValueEncoder
+from lcs.representations import Interval, FULL_INTERVAL
 
 
 class TestGeneticAlgorithm:
@@ -15,21 +14,20 @@ class TestGeneticAlgorithm:
     @pytest.fixture
     def cfg(self):
         return Configuration(classifier_length=2,
-                             number_of_possible_actions=2,
-                             encoder=RealValueEncoder(4))
+                             number_of_possible_actions=2)
 
     @pytest.mark.parametrize("_cond, _effect", [
-        ([UBR(2, 5), UBR(5, 10)], [UBR(2, 5), UBR(5, 10)]),
-        ([UBR(5, 2), UBR(10, 5)], [UBR(5, 2), UBR(10, 5)]),
-        ([UBR(2, 2), UBR(5, 5)], [UBR(2, 2), UBR(5, 5)]),
-        ([UBR(0, 15), UBR(0, 15)], [UBR(0, 15), UBR(0, 15)]),
+        ([Interval(.2, .5), Interval(.5, .7)],
+         [Interval(.2, .5), Interval(.5, .7)]),
+        ([Interval(.5, .2), Interval(.8, .5)],
+         [Interval(.5, .2), Interval(.8, .5)])
     ])
-    def test_aggressive_mutation(self, _cond, _effect, cfg):
+    def test_if_elements_are_mutated(self, _cond, _effect, cfg):
         # given
+        np.random.seed(12345)
         condition = Condition(_cond, cfg)
         effect = Effect(_effect, cfg)
 
-        cfg.encoder = RealValueEncoder(16)  # more precise encoder
         cfg.mutation_noise = 0.5  # strong noise mutation range
         mu = 1.0  # mutate every attribute
 
@@ -42,25 +40,49 @@ def test_aggressive_mutation(self, _cond, _effect, cfg):
         mutate(cl, mu)
 
         # then
-        range_min, range_max = cfg.encoder.range
         for idx, (c, e) in enumerate(zip(cl.condition, cl.effect)):
-            # assert that we have new locus
-            if condition[idx] != cfg.classifier_wildcard:
-                assert condition[idx] != c
+            assert condition[idx] != c
+            assert effect[idx] != e
 
-            if effect[idx] != cfg.classifier_wildcard:
-                assert effect[idx] != e
+    @pytest.mark.parametrize("_cond, _effect", [
+        ([Interval(.2, .5), Interval(.5, 1.)],
+         [Interval(.2, .5), Interval(.5, 1.)]),
+        ([Interval(.5, .2), Interval(.8, .5)],
+         [Interval(.5, .2), Interval(.8, .5)]),
+        ([Interval(.2, .2), Interval(.3, .3)],
+         [Interval(.2, .2), Interval(.3, .3)]),
+        ([Interval(0., 1.), Interval(0., 1.)],
+         [Interval(0., 1.), Interval(0., 1.)]),
+    ])
+    def test_if_range_is_not_exceeded_in_mutation(self, _cond, _effect, cfg):
+        # given
+        condition = Condition(_cond, cfg)
+        effect = Effect(_effect, cfg)
+
+        cfg.mutation_noise = 0.5  # strong noise mutation range
+        mu = 1.0  # mutate every attribute
+
+        cl = Classifier(
+            condition=deepcopy(condition),
+            effect=deepcopy(effect),
+            cfg=cfg)
 
+        # when
+        mutate(cl, mu)
+
+        # then
+        for idx, (c, e) in enumerate(zip(cl.condition, cl.effect)):
             # assert if condition values are in ranges
-            assert c.lower_bound >= range_min
-            assert c.upper_bound <= range_max
+            assert c.left >= FULL_INTERVAL.left
+            assert c.right <= FULL_INTERVAL.right
 
             # assert if effect values are in ranges
-            assert e.lower_bound >= range_min
-            assert e.upper_bound <= range_max
+            assert e.left >= FULL_INTERVAL.left
+            assert e.right <= FULL_INTERVAL.right
 
     @pytest.mark.parametrize("_cond, _effect", [
-        ([UBR(2, 5), UBR(5, 10)], [UBR(3, 6), UBR(1, 1)]),
+        ([Interval(.2, .5), Interval(.5, .8)],
+         [Interval(.2, .5), Interval(.5, .8)]),
     ])
     def test_disabled_mutation(self, _cond, _effect, cfg):
         # given
@@ -77,20 +99,24 @@ def test_disabled_mutation(self, _cond, _effect, cfg):
 
         # then
         for idx, (c, e) in enumerate(zip(cl.condition, cl.effect)):
-            assert c.lower_bound == condition[idx].lower_bound
-            assert c.upper_bound == condition[idx].upper_bound
-            assert e.lower_bound == effect[idx].lower_bound
-            assert e.upper_bound == effect[idx].upper_bound
+            assert c.left == condition[idx].left
+            assert c.right == condition[idx].right
+            assert e.left == effect[idx].left
+            assert e.right == effect[idx].right
 
     def test_crossover(self, cfg):
         # given
         parent = Classifier(
-            condition=Condition([UBR(1, 1), UBR(1, 1), UBR(1, 1)], cfg),
-            effect=Effect([UBR(1, 1), UBR(1, 1), UBR(1, 1)], cfg),
+            condition=Condition(
+                [Interval(.1, .1), Interval(.1, .1), Interval(.1, .1)], cfg),
+            effect=Effect(
+                [Interval(.1, .1), Interval(.1, .1), Interval(.1, .1)], cfg),
             cfg=cfg)
         donor = Classifier(
-            condition=Condition([UBR(2, 2), UBR(2, 2), UBR(2, 2)], cfg),
-            effect=Effect([UBR(2, 2), UBR(2, 2), UBR(2, 2)], cfg),
+            condition=Condition(
+                [Interval(.2, .2), Interval(.2, .2), Interval(.2, .2)], cfg),
+            effect=Effect(
+                [Interval(.2, .2), Interval(.2, .2), Interval(.2, .2)], cfg),
             cfg=cfg)
 
         # when
@@ -99,29 +125,33 @@ def test_crossover(self, cfg):
 
         # then
         assert parent.condition == \
-            Condition([UBR(1, 1), UBR(1, 2), UBR(2, 2)], cfg)
+            Condition(
+                [Interval(.1, .1), Interval(.1, .2), Interval(.2, .2)], cfg)
         assert parent.effect == \
-            Effect([UBR(1, 1), UBR(1, 2), UBR(2, 2)], cfg)
+            Effect(
+                [Interval(.1, .1), Interval(.1, .2), Interval(.2, .2)], cfg)
         assert donor.condition == \
-            Condition([UBR(2, 2), UBR(2, 1), UBR(1, 1)], cfg)
+            Condition(
+                [Interval(.2, .2), Interval(.2, .1), Interval(.1, .1)], cfg)
         assert donor.effect == \
-            Effect([UBR(2, 2), UBR(2, 1), UBR(1, 1)], cfg)
+            Effect(
+                [Interval(.2, .2), Interval(.2, .1), Interval(.1, .1)], cfg)
 
     @pytest.mark.parametrize("_cond, _result", [
-        ([UBR(1, 3), UBR(2, 4)], [1, 3, 2, 4])
+        ([Interval(.1, .3), Interval(.2, .4)], [.1, .3, .2, .4])
     ])
     def test_should_flatten_condition(self, _cond, _result, cfg):
         assert _flatten(Condition(_cond, cfg=cfg)) == _result
 
     @pytest.mark.parametrize("_effect, _result", [
-        ([UBR(1, 3), UBR(2, 4)], [1, 3, 2, 4])
+        ([Interval(.1, .3), Interval(.2, .4)], [.1, .3, .2, .4])
     ])
     def test_should_flatten_effect(self, _effect, _result, cfg):
         assert _flatten(Effect(_effect, cfg=cfg)) == _result
 
     @pytest.mark.parametrize("_flat, _result", [
-        ([1, 3], [UBR(1, 3)]),
-        ([1, 3, 2, 4], [UBR(1, 3), UBR(2, 4)])
+        ([.1, .3], [Interval(.1, .3)]),
+        ([.1, .3, .2, .4], [Interval(.1, .3), Interval(.2, .4)])
     ])
     def test_should_unflatten(self, _flat, _result):
         assert _unflatten(_flat) == _result
diff --git a/tests/lcs/agents/racs/test_Classifier.py b/tests/lcs/agents/racs/test_Classifier.py
index 63c1a079..4b27f937 100644
--- a/tests/lcs/agents/racs/test_Classifier.py
+++ b/tests/lcs/agents/racs/test_Classifier.py
@@ -5,8 +5,7 @@
 
 from lcs import Perception
 from lcs.agents.racs import Configuration, Condition, Effect, Classifier
-from lcs.representations import UBR
-from lcs.representations.RealValueEncoder import RealValueEncoder
+from lcs.representations import Interval
 
 
 class TestClassifier:
@@ -14,8 +13,7 @@ class TestClassifier:
     @pytest.fixture
     def cfg(self):
         return Configuration(classifier_length=2,
-                             number_of_possible_actions=2,
-                             encoder=RealValueEncoder(4))
+                             number_of_possible_actions=2)
 
     def test_should_initialize_without_arguments(self, cfg):
         # when
@@ -31,38 +29,19 @@ def test_should_initialize_without_arguments(self, cfg):
 
     def test_should_detect_identical_classifier(self, cfg):
         cl_1 = Classifier(
-            condition=Condition([UBR(0, 1), UBR(0, 2)], cfg=cfg),
+            condition=Condition([Interval(0., 1.), Interval(0., 1.)], cfg=cfg),
             action=1,
-            effect=Effect([UBR(2, 3), UBR(4, 5)], cfg=cfg),
+            effect=Effect([Interval(.2, .3), Interval(.4, .5)], cfg=cfg),
             cfg=cfg)
 
         cl_2 = Classifier(
-            condition=Condition([UBR(0, 1), UBR(0, 2)], cfg=cfg),
+            condition=Condition([Interval(0., 1.), Interval(0., 1.)], cfg=cfg),
             action=1,
-            effect=Effect([UBR(2, 3), UBR(4, 5)], cfg=cfg),
+            effect=Effect([Interval(.2, .3), Interval(.4, .5)], cfg=cfg),
             cfg=cfg)
 
         assert cl_1 == cl_2
 
-    def test_should_find_similar(self):
-        # given
-        cfg = Configuration(3, 2, encoder=RealValueEncoder(2))
-        cl1 = Classifier(
-            condition=Condition([UBR(0, 0), UBR(0, 3), UBR(0, 3)], cfg=cfg),
-            action=0,
-            effect=Effect([UBR(0, 3), UBR(0, 3), UBR(0, 3)], cfg=cfg),
-            cfg=cfg
-        )
-        cl2 = Classifier(
-            condition=Condition([UBR(0, 0), UBR(0, 3), UBR(0, 3)], cfg=cfg),
-            action=0,
-            effect=Effect([UBR(0, 3), UBR(0, 3), UBR(0, 3)], cfg=cfg),
-            cfg=cfg
-        )
-
-        # then
-        assert cl1 == cl2
-
     @pytest.mark.parametrize("_q, _r, _fitness", [
         (0.0, 0.0, 0.0),
         (0.3, 0.5, 0.15),
@@ -74,10 +53,10 @@ def test_should_calculate_fitness(self, _q, _r, _fitness, cfg):
     @pytest.mark.parametrize("_effect, _p0, _p1, _result", [
         # Classifier with default pass-through effect
         (None, [0.5, 0.5], [0.5, 0.5], True),
-        ([UBR(0, 15), UBR(10, 12)], [0.5, 0.5], [0.5, 0.5], False),
-        ([UBR(0, 4), UBR(10, 12)], [0.8, 0.8], [0.2, 0.7], True),
-        # second perception attribute is unchanged - should be a wildcard
-        ([UBR(0, 4), UBR(10, 12)], [0.8, 0.8], [0.2, 0.8], False),
+        ([Interval(0., 1.), Interval(.7, .8)], [0.5, 0.5], [0.5, 0.5], False),
+        ([Interval(0., .3), Interval(.6, .8)], [0.8, 0.8], [0.2, 0.7], True),
+        # second effect attribute is unchanged - should be a wildcard
+        ([Interval(0., .3), Interval(.7, .9)], [0.8, 0.8], [0.2, 0.8], False),
     ])
     def test_should_anticipate_change(self, _effect, _p0, _p1, _result, cfg):
         # given
@@ -137,11 +116,16 @@ def test_should_decrease_quality(self, cfg):
         assert cl.q == 0.475
 
     @pytest.mark.parametrize("_condition, _effect, _sua", [
-        ([UBR(4, 15), UBR(2, 15)], [UBR(0, 15), UBR(0, 15)], 2),
-        ([UBR(4, 15), UBR(0, 15)], [UBR(0, 15), UBR(0, 15)], 1),
-        ([UBR(0, 15), UBR(0, 15)], [UBR(0, 15), UBR(0, 15)], 0),
-        ([UBR(4, 15), UBR(0, 15)], [UBR(0, 15), UBR(5, 15)], 1),
-        ([UBR(4, 15), UBR(6, 15)], [UBR(4, 15), UBR(6, 15)], 0),
+        ([Interval(.4, 1.), Interval(.2, 1.)],
+         [Interval(0., 1.), Interval(0., 1.)], 2),
+        ([Interval(.4, 1.), Interval(0., 1.)],
+         [Interval(0., 1.), Interval(0., 1.)], 1),
+        ([Interval(0., 1.), Interval(0., 1.)],
+         [Interval(0., 1.), Interval(0., 1.)], 0),
+        ([Interval(.4, 1.), Interval(0., 1.)],
+         [Interval(0., 1.), Interval(.5, 1.)], 1),
+        ([Interval(.4, 1.), Interval(.6, 1.)],
+         [Interval(.4, 1.), Interval(.6, 1.)], 0),
     ])
     def test_should_count_specified_unchanging_attributes(
             self, _condition, _effect, _sua, cfg):
@@ -157,10 +141,12 @@ def test_should_count_specified_unchanging_attributes(
     def test_should_create_copy(self, cfg):
         # given
         operation_time = random.randint(0, 100)
-        condition = Condition([self._random_ubr(), self._random_ubr()],
+        condition = Condition([self._random_interval(),
+                               self._random_interval()],
                               cfg=cfg)
         action = random.randint(0, 2)
-        effect = Effect([self._random_ubr(), self._random_ubr()], cfg=cfg)
+        effect = Effect([self._random_interval(),
+                         self._random_interval()], cfg=cfg)
 
         cl = Classifier(condition, action, effect,
                         quality=random.random(),
@@ -172,11 +158,19 @@ def test_should_create_copy(self, cfg):
 
         # then
         assert cl is not copied_cl
+
         assert cl.condition == copied_cl.condition
         assert cl.condition is not copied_cl.condition
+        assert cl.condition[0] == copied_cl.condition[0]
+        assert cl.condition[0] is not copied_cl.condition[0]
+
         assert cl.action == copied_cl.action
+
         assert cl.effect == copied_cl.effect
         assert cl.effect is not copied_cl.effect
+        assert cl.effect[0] == copied_cl.effect[0]
+        assert cl.effect[0] is not copied_cl.effect[0]
+
         assert copied_cl.is_marked() is False
         assert cl.r == copied_cl.r
         assert cl.q == copied_cl.q
@@ -185,33 +179,34 @@ def test_should_create_copy(self, cfg):
 
     def test_should_specialize(self, cfg):
         # given
-        p0 = Perception(np.random.random(2), oktypes=(float,))
-        p1 = Perception(np.random.random(2), oktypes=(float,))
+        p0 = Perception(np.random.random(2).tolist(), oktypes=(float,))
+        p1 = Perception(np.random.random(2).tolist(), oktypes=(float,))
         cl = Classifier(cfg=cfg)
 
         # when
         cl.specialize(p0, p1)
 
         # then
-        enc_p0 = list(map(cfg.encoder.encode, p0))
-        enc_p1 = list(map(cfg.encoder.encode, p1))
-
-        for i, (c_ubr, e_ubr) in enumerate(zip(cl.condition, cl.effect)):
-            assert c_ubr.lower_bound <= enc_p0[i] <= c_ubr.upper_bound
-            assert e_ubr.lower_bound <= enc_p1[i] <= e_ubr.upper_bound
+        for i, (c_int, e_int) in enumerate(zip(cl.condition, cl.effect)):
+            assert p0[i] in c_int
+            assert p1[i] in e_int
 
     @pytest.mark.parametrize("_condition, _effect, _soa_before, _soa_after", [
-        ([UBR(4, 15), UBR(2, 15)], [UBR(0, 15), UBR(0, 15)], 2, 1),
-        ([UBR(4, 15), UBR(0, 15)], [UBR(0, 15), UBR(0, 15)], 1, 0),
-        ([UBR(0, 15), UBR(0, 15)], [UBR(0, 15), UBR(0, 15)], 0, 0),
+        ([Interval(.4, 1.), Interval(.2, 1.)],
+         [Interval(0., 1.), Interval(0., 1.)], 2, 1),
+        ([Interval(.4, 1.), Interval(0., 1.)],
+         [Interval(0., 1.), Interval(0., 1.)], 1, 0),
+        ([Interval(0., 1.), Interval(0., 1.)],
+         [Interval(0., 1.), Interval(0., 1.)], 0, 0),
     ])
     def test_should_generalize_randomly_unchanging_condition_attribute(
             self, _condition, _effect, _soa_before, _soa_after, cfg):
 
         # given
-        condition = Condition(_condition, cfg)
-        effect = Effect(_effect, cfg)
-        cl = Classifier(condition=condition, effect=effect, cfg=cfg)
+        cl = Classifier(condition=Condition(_condition, cfg),
+                        effect=Effect(_effect, cfg),
+                        cfg=cfg)
+
         assert len(cl.specified_unchanging_attributes) == _soa_before
 
         # when
@@ -221,10 +216,13 @@ def test_should_generalize_randomly_unchanging_condition_attribute(
         assert (len(cl.specified_unchanging_attributes)) == _soa_after
 
     @pytest.mark.parametrize("_c1, _c2, _result", [
-        ([UBR(4, 6), UBR(1, 5)], [UBR(4, 6), UBR(1, 4)], True),
-        ([UBR(4, 6), UBR(1, 5)], [UBR(4, 6), UBR(1, 6)], False),
+        ([Interval(.4, .6), Interval(.1, .5)],
+         [Interval(.4, .6), Interval(.1, .4)], True),
+        ([Interval(.4, .6), Interval(.1, .5)],
+         [Interval(.4, .6), Interval(.1, .6)], False),
         # The same classifiers
-        ([UBR(4, 6), UBR(1, 5)], [UBR(4, 6), UBR(1, 5)], False)
+        ([Interval(.4, .6), Interval(.1, .5)],
+         [Interval(.4, .6), Interval(.1, .5)], False)
     ])
     def test_should_find_more_general(self, _c1, _c2, _result, cfg):
         # given
@@ -235,11 +233,11 @@ def test_should_find_more_general(self, _c1, _c2, _result, cfg):
         assert cl1.is_more_general(cl2) is _result
 
     @pytest.mark.parametrize("_cond, _res", [
-        ([UBR(4, 6), UBR(1, 5)], {1: 2, 2: 0, 3: 0, 4: 0}),
-        ([UBR(0, 6), UBR(1, 5)], {1: 1, 2: 1, 3: 0, 4: 0}),
-        ([UBR(0, 4), UBR(6, 15)], {1: 0, 2: 1, 3: 1, 4: 0}),
-        ([UBR(0, 15), UBR(6, 14)], {1: 1, 2: 0, 3: 0, 4: 1}),
-        ([UBR(0, 15), UBR(0, 15)], {1: 0, 2: 0, 3: 0, 4: 2}),
+        ([Interval(.4, .6), Interval(.1, .5)], {1: 2, 2: 0, 3: 0, 4: 0}),
+        ([Interval(0., .6), Interval(.1, .5)], {1: 1, 2: 1, 3: 0, 4: 0}),
+        ([Interval(0., .4), Interval(.6, 1.)], {1: 0, 2: 1, 3: 1, 4: 0}),
+        ([Interval(0., 1.), Interval(.4, .9)], {1: 1, 2: 0, 3: 0, 4: 1}),
+        ([Interval(0., 1.), Interval(0., 1.)], {1: 0, 2: 0, 3: 0, 4: 2}),
     ])
     def test_count_regions(self, _cond, _res, cfg):
         # given
@@ -249,5 +247,5 @@ def test_count_regions(self, _cond, _res, cfg):
         assert cl.get_interval_proportions() == _res
 
     @staticmethod
-    def _random_ubr(lower=0, upper=15):
-        return UBR(random.randint(lower, upper), random.randint(lower, upper))
+    def _random_interval():
+        return Interval(*np.random.random(2).tolist())
diff --git a/tests/lcs/agents/racs/test_ClassifierList.py b/tests/lcs/agents/racs/test_ClassifierList.py
index 081850d8..fe5aa767 100644
--- a/tests/lcs/agents/racs/test_ClassifierList.py
+++ b/tests/lcs/agents/racs/test_ClassifierList.py
@@ -2,18 +2,16 @@
 
 from lcs import Perception
 from lcs.agents.racs import Configuration, Condition, \
-    Classifier, ClassifierList, Effect
-from lcs.representations import UBR
-from lcs.representations.RealValueEncoder import RealValueEncoder
+    Classifier, ClassifiersList, Effect
+from lcs.representations import Interval
 
 
-class TestClassifierList:
+class TestClassifiersList:
 
     @pytest.fixture
     def cfg(self):
         return Configuration(classifier_length=2,
-                             number_of_possible_actions=2,
-                             encoder=RealValueEncoder(4))
+                             number_of_possible_actions=2)
 
     def test_should_initialize_classifier_list(self, cfg):
         # given
@@ -22,7 +20,7 @@ def test_should_initialize_classifier_list(self, cfg):
         cl3 = Classifier(action=3, cfg=cfg)
 
         # when
-        cll = ClassifierList(*[cl1, cl2])
+        cll = ClassifiersList(*[cl1, cl2])
 
         # then
         assert len(cll) == 2
@@ -32,16 +30,17 @@ def test_should_initialize_classifier_list(self, cfg):
 
     def test_should_form_match_set(self, cfg):
         # given
-        # 4bit encoding 0.2 => 3, 0.6 => 9
-        observation = Perception([0.2, 0.6], oktypes=(float,))
+        observation = Perception([.2, .6], oktypes=(float,))
 
-        cl1 = Classifier(condition=Condition([UBR(2, 5), UBR(8, 11)], cfg=cfg),
-                         cfg=cfg)
-        cl2 = Classifier(condition=Condition([UBR(5, 7), UBR(5, 12)], cfg=cfg),
-                         cfg=cfg)
+        cl1 = Classifier(
+            condition=Condition([Interval(.1, .3), Interval(.5, .7)], cfg=cfg),
+            cfg=cfg)
+        cl2 = Classifier(
+            condition=Condition([Interval(.1, .4), Interval(.2, .3)], cfg=cfg),
+            cfg=cfg)
         cl3 = Classifier(cfg=cfg)
 
-        population = ClassifierList(*[cl1, cl2, cl3])
+        population = ClassifiersList(*[cl1, cl2, cl3])
 
         # when
         match_set = population.form_match_set(observation)
@@ -58,7 +57,7 @@ def test_should_form_action_set(self, cfg):
         cl2 = Classifier(action=0, cfg=cfg)
         cl3 = Classifier(action=1, cfg=cfg)
 
-        population = ClassifierList(*[cl1, cl2, cl3])
+        population = ClassifiersList(*[cl1, cl2, cl3])
 
         # when
         action_set = population.form_action_set(0)
@@ -75,7 +74,7 @@ def test_should_expand(self, cfg):
         cl2 = Classifier(numerosity=2, cfg=cfg)
         cl3 = Classifier(numerosity=3, cfg=cfg)
 
-        population = ClassifierList(*[cl1, cl2, cl3])
+        population = ClassifiersList(*[cl1, cl2, cl3])
 
         # when
         expanded = population.expand()
@@ -90,23 +89,23 @@ def test_should_get_maximum_fitness(self, cfg):
         # given
         # anticipate change - low fitness
         cl1 = Classifier(
-            effect=Effect([UBR(0, 1), UBR(0, 3)], cfg),
+            effect=Effect([Interval(0., 1.), Interval(0., .3)], cfg),
             quality=0.3, reward=1,
             cfg=cfg)
 
         # do not anticipate change - high fitness
         cl2 = Classifier(
-            effect=Effect([UBR(0, 15), UBR(0, 15)], cfg),
+            effect=Effect([Interval(0., 1.), Interval(0., 1.)], cfg),
             quality=0.5, reward=1,
             cfg=cfg)
 
         # anticipate change - medium fitness
         cl3 = Classifier(
-            effect=Effect([UBR(0, 14), UBR(0, 15)], cfg),
+            effect=Effect([Interval(0., .9), Interval(0., 1.)], cfg),
             quality=0.4, reward=1,
             cfg=cfg)
 
-        population = ClassifierList(*[cl1, cl2, cl3])
+        population = ClassifiersList(*[cl1, cl2, cl3])
 
         # when
         mf = population.get_maximum_fitness()
@@ -117,16 +116,16 @@ def test_should_get_maximum_fitness(self, cfg):
     def test_should_return_zero_max_fitness(self, cfg):
         # given classifiers that does not anticipate change
         cl1 = Classifier(
-            effect=Effect([UBR(0, 15), UBR(0, 15)], cfg),
+            effect=Effect([Interval(0., 1.), Interval(0., 1.)], cfg),
             quality=0.5, reward=1,
             cfg=cfg)
 
         cl2 = Classifier(
-            effect=Effect([UBR(0, 15), UBR(0, 15)], cfg),
+            effect=Effect([Interval(0., 1.), Interval(0., 1.)], cfg),
             quality=0.7, reward=1,
             cfg=cfg)
 
-        population = ClassifierList(*[cl1, cl2])
+        population = ClassifiersList(*[cl1, cl2])
 
         # when
         mf = population.get_maximum_fitness()
@@ -141,10 +140,10 @@ def test_should_apply_reinforcement_learning(self, cfg):
             immediate_reward=11.29,
             cfg=cfg)
 
-        population = ClassifierList(*[cl])
+        population = ClassifiersList(*[cl])
 
         # when
-        ClassifierList.apply_reinforcement_learning(
+        ClassifiersList.apply_reinforcement_learning(
             population, 0, 28.79, cfg.beta, cfg.gamma)
 
         # then
diff --git a/tests/lcs/agents/racs/test_Condition.py b/tests/lcs/agents/racs/test_Condition.py
index 46306d1b..f3a10732 100644
--- a/tests/lcs/agents/racs/test_Condition.py
+++ b/tests/lcs/agents/racs/test_Condition.py
@@ -2,8 +2,7 @@
 
 from lcs import Perception
 from lcs.agents.racs import Configuration, Condition
-from lcs.representations import UBR
-from lcs.representations.RealValueEncoder import RealValueEncoder
+from lcs.representations import Interval
 
 
 class TestCondition:
@@ -11,8 +10,7 @@ class TestCondition:
     @pytest.fixture
     def cfg(self):
         return Configuration(classifier_length=2,
-                             number_of_possible_actions=2,
-                             encoder=RealValueEncoder(4))
+                             number_of_possible_actions=2)
 
     def test_should_create_generic_condition(self, cfg):
         # when
@@ -24,12 +22,12 @@ def test_should_create_generic_condition(self, cfg):
             assert allele == cfg.classifier_wildcard
 
     @pytest.mark.parametrize("_init_cond, _other_cond, _result_cond", [
-        ([UBR(0, 10), UBR(5, 2)],
-         [UBR(0, 15), UBR(0, 15)],
-         [UBR(0, 10), UBR(2, 5)]),
-        ([UBR(0, 10), UBR(5, 2)],
-         [UBR(3, 12), UBR(0, 15)],
-         [UBR(3, 12), UBR(2, 5)])
+        ([Interval(0., .8), Interval(.5, .2)],
+         [Interval(0., 1.), Interval(0., 1.)],
+         [Interval(0., .8), Interval(.2, .5)]),
+        ([Interval(0., .8), Interval(.5, .2)],
+         [Interval(.2, .9), Interval(0., 1.)],
+         [Interval(.2, .9), Interval(.2, .5)])
     ])
     def test_should_specialize_with_condition(
             self, _init_cond, _other_cond, _result_cond, cfg):
@@ -45,8 +43,10 @@ def test_should_specialize_with_condition(
         assert cond == Condition(_result_cond, cfg)
 
     @pytest.mark.parametrize("_condition, _idx, _generalized", [
-        ([UBR(1, 4), UBR(5, 7)], 0, [UBR(0, 15), UBR(5, 7)]),
-        ([UBR(1, 4), UBR(5, 7)], 1, [UBR(1, 4), UBR(0, 15)])
+        ([Interval(.1, .4), Interval(.5, .7)], 0,
+         [Interval(0., 1.), Interval(.5, .7)]),
+        ([Interval(.1, .4), Interval(.5, .7)], 1,
+         [Interval(.1, .4), Interval(0., 1.)])
     ])
     def test_generalize(self, _condition, _idx, _generalized, cfg):
         # given
@@ -59,9 +59,9 @@ def test_generalize(self, _condition, _idx, _generalized, cfg):
         assert cond == Condition(_generalized, cfg)
 
     @pytest.mark.parametrize("_condition, _spec_before, _spec_after", [
-        ([UBR(2, 6), UBR(7, 2)], 2, 1),
-        ([UBR(2, 6), UBR(0, 15)], 1, 0),
-        ([UBR(0, 15), UBR(0, 15)], 0, 0),
+        ([Interval(.2, .6), Interval(.7, .2)], 2, 1),
+        ([Interval(.2, .6), Interval(0., 1.)], 1, 0),
+        ([Interval(0., 1.), Interval(0., 1.)], 0, 0),
     ])
     def test_should_generalize_specific_attributes_randomly(
             self, _condition, _spec_before, _spec_after, cfg):
@@ -77,29 +77,30 @@ def test_should_generalize_specific_attributes_randomly(
         assert condition.specificity == _spec_after
 
     @pytest.mark.parametrize("_condition, _specificity", [
-        ([UBR(0, 15), UBR(0, 15)], 0),
-        ([UBR(0, 15), UBR(2, 15)], 1),
-        ([UBR(5, 15), UBR(2, 12)], 2)
+        ([Interval(0., 1.), Interval(0., 1.)], 0),
+        ([Interval(0., 1.), Interval(.2, 1.)], 1),
+        ([Interval(.5, 1.), Interval(.2, .8)], 2)
     ])
     def test_should_count_specificity(self, _condition, _specificity, cfg):
         cond = Condition(_condition, cfg=cfg)
         assert cond.specificity == _specificity
 
     @pytest.mark.parametrize("_condition, _covered_pct", [
-        ([UBR(0, 15), UBR(0, 15)], 1.0),
-        ([UBR(7, 7), UBR(4, 4)], 0.0625),
-        ([UBR(7, 8), UBR(4, 5)], 0.125),
-        ([UBR(2, 8), UBR(4, 10)], 0.4375),
+        ([Interval(0., 1.), Interval(0., 1.)], 1.0),
+        ([Interval(0., .5), Interval(.5, 1.)], 0.5),
+        ([Interval(.7, .8), Interval(.4, .5)], 0.1),
+        ([Interval(.2, .8), Interval(.7, .8)], 0.35),
     ])
     def test_should_calculate_cover_ratio(
             self, _condition, _covered_pct, cfg):
         cond = Condition(_condition, cfg=cfg)
-        assert cond.cover_ratio == _covered_pct
+        assert abs(cond.cover_ratio - _covered_pct) < 0.00001
 
     @pytest.mark.parametrize("_condition, _perception, _result", [
-        ([UBR(0, 15), UBR(0, 15)], [0.2, 0.4], True),
-        ([UBR(0, 15), UBR(0, 2)], [0.5, 0.5], False),
-        ([UBR(8, 8), UBR(10, 10)], [0.5, 0.65], True)
+        ([Interval(0., 1.), Interval(0., 1.)], [0.2, 0.4], True),
+        ([Interval(0., 1.), Interval(0., .2)], [0.5, 0.5], False),
+        ([Interval(.5, .55), Interval(.65, .75)], [0.5, 0.65], True),
+        ([Interval(.49, .51), Interval(.64, .66)], [0.5, 0.65], True),
     ])
     def test_should_match_perception(
             self, _condition, _perception, _result, cfg):
@@ -112,10 +113,16 @@ def test_should_match_perception(
         assert cond.does_match(p0) == _result
 
     @pytest.mark.parametrize("_cond1, _cond2, _result", [
-        ([UBR(0, 15), UBR(0, 15)], [UBR(2, 4), UBR(5, 10)], True),
-        ([UBR(6, 10), UBR(0, 15)], [UBR(2, 4), UBR(5, 10)], False),
-        ([UBR(0, 15), UBR(4, 10)], [UBR(2, 4), UBR(6, 12)], False),
-        ([UBR(2, 4), UBR(5, 5)], [UBR(2, 4), UBR(5, 5)], True),
+        ([Interval(0., 1.), Interval(0., 1.)],
+         [Interval(.2, .4), Interval(.5, .8)], True),
+        ([Interval(.7, .9), Interval(0., 1.)],
+         [Interval(.2, .4), Interval(.5, .8)], False),
+        ([Interval(0., 1.), Interval(.4, .6)],
+         [Interval(.2, .4), Interval(.5, .9)], False),
+        ([Interval(.2, .4), Interval(.5, .5)],
+         [Interval(.2, .4), Interval(.5, .5)], True),
+        ([Interval(.2, .4), Interval(.5, .5)],
+         [Interval(.5, .5), Interval(.2, .4)], False),
     ])
     def test_should_subsume_condition(self, _cond1, _cond2, _result, cfg):
         # given
@@ -125,8 +132,8 @@ def test_should_subsume_condition(self, _cond1, _cond2, _result, cfg):
         # then
         assert cond1.subsumes(cond2) == _result
 
-    @pytest.mark.parametrize("_cond, _result", [
-        ([UBR(0, 15), UBR(0, 7)], 'OOOOOOOOOO|OOOOO.....')
-    ])
-    def test_should_visualize(self, _cond, _result, cfg):
-        assert repr(Condition(_cond, cfg=cfg)) == _result
+    # @pytest.mark.parametrize("_cond, _result", [
+    #     ([UBR(0, 15), UBR(0, 7)], 'OOOOOOOOOO|OOOOO.....')
+    # ])
+    # def test_should_visualize(self, _cond, _result, cfg):
+    #     assert repr(Condition(_cond, cfg=cfg)) == _result
diff --git a/tests/lcs/agents/racs/test_Effect.py b/tests/lcs/agents/racs/test_Effect.py
index 5675715b..13e22f36 100644
--- a/tests/lcs/agents/racs/test_Effect.py
+++ b/tests/lcs/agents/racs/test_Effect.py
@@ -2,8 +2,7 @@
 
 from lcs import Perception
 from lcs.agents.racs import Configuration, Effect
-from lcs.representations import UBR
-from lcs.representations.RealValueEncoder import RealValueEncoder
+from lcs.representations import Interval
 
 
 class TestEffect:
@@ -11,13 +10,12 @@ class TestEffect:
     @pytest.fixture
     def cfg(self):
         return Configuration(classifier_length=2,
-                             number_of_possible_actions=2,
-                             encoder=RealValueEncoder(4))
+                             number_of_possible_actions=2)
 
     @pytest.mark.parametrize("_e, _result", [
-        ([UBR(0, 15), UBR(0, 15)], False),
-        ([UBR(0, 15), UBR(2, 14)], True),
-        ([UBR(4, 10), UBR(2, 14)], True),
+        ([Interval(0., 1.), Interval(0., 1.)], False),
+        ([Interval(0., 1.), Interval(.1, .3)], True),
+        ([Interval(.4, .8), Interval(.4, .9)], True),
     ])
     def test_should_detect_change(self, _e, _result, cfg):
         assert Effect(_e, cfg).specify_change == _result
@@ -33,15 +31,15 @@ def test_should_create_pass_through_effect(self, cfg):
 
     @pytest.mark.parametrize("_p0, _p1, _effect, is_specializable", [
         # Effect is all pass-through. Can be specialized.
-        ([0.5, 0.5], [0.5, 0.5], [UBR(0, 15), UBR(0, 15)], True),
+        ([0.5, 0.5], [0.5, 0.5], [Interval(0., 1.), Interval(0., 1.)], True),
         # 1 pass-through effect get skipped. Second effect attribute get's
         # examined. P1 perception is not in correct range. That's invalid
-        ([0.5, 0.5], [0.5, 0.5], [UBR(0, 15), UBR(2, 4)], False),
+        ([0.5, 0.5], [0.5, 0.5], [Interval(0., 1.), Interval(.2, .4)], False),
         # In this case the range is proper, but no change is anticipated.
         # In this case this should be a pass-through symbol.
-        ([0.5, 0.5], [0.5, 0.5], [UBR(0, 15), UBR(2, 12)], False),
+        ([0.5, 0.5], [0.5, 0.5], [Interval(0., 1.), Interval(.2, .8)], False),
         # Here second perception attribute changes. 0.8 => 12
-        ([0.5, 0.5], [0.5, 0.8], [UBR(0, 15), UBR(10, 14)], True)
+        ([0.5, 0.5], [0.5, 0.8], [Interval(0., 1.), Interval(.75, .85)], True)
     ])
     def test_should_specialize(self, _p0, _p1, _effect, is_specializable, cfg):
         # given
@@ -53,10 +51,14 @@ def test_should_specialize(self, _p0, _p1, _effect, is_specializable, cfg):
         assert effect.is_specializable(p0, p1) is is_specializable
 
     @pytest.mark.parametrize("_effect1, _effect2, _result", [
-        ([UBR(0, 15), UBR(0, 15)], [UBR(2, 4), UBR(5, 10)], True),
-        ([UBR(6, 10), UBR(0, 15)], [UBR(2, 4), UBR(5, 10)], False),
-        ([UBR(0, 15), UBR(4, 10)], [UBR(2, 4), UBR(6, 12)], False),
-        ([UBR(2, 4), UBR(5, 5)], [UBR(2, 4), UBR(5, 5)], True),
+        ([Interval(0., 1.), Interval(0., 1.)],
+         [Interval(.2, .4), Interval(.5, .8)], True),
+        ([Interval(.7, .9), Interval(0., 1.)],
+         [Interval(.2, .4), Interval(.7, .8)], False),
+        ([Interval(0., 1.), Interval(.3, .7)],
+         [Interval(.2, .4), Interval(.4, .8)], False),
+        ([Interval(.2, .4), Interval(.5, .5)],
+         [Interval(.4, .2), Interval(.5, .5)], True),
     ])
     def test_should_subsume_effect(self, _effect1, _effect2, _result, cfg):
         # given
@@ -66,8 +68,23 @@ def test_should_subsume_effect(self, _effect1, _effect2, _result, cfg):
         # then
         assert effect1.subsumes(effect2) == _result
 
-    @pytest.mark.parametrize("_effect, _result", [
-        ([UBR(0, 15), UBR(0, 7)], 'OOOOOOOOOO|OOOOO.....')
+    @pytest.mark.parametrize("_effect1, _effect2, _result", [
+        ([Interval(0., 1.), Interval(0., 1.)],
+         [Interval(.01, 1.), Interval(0., .99)], True),
+        ([Interval(0., 1.), Interval(0., 1.)],
+         [Interval(.5, .7), Interval(0., 1.)], False)
     ])
-    def test_should_visualize(self, _effect, _result, cfg):
-        assert repr(Effect(_effect, cfg=cfg)) == _result
+    def test_should_detect_equal(self, _effect1, _effect2, _result, cfg):
+        # given
+        effect1 = Effect(_effect1, cfg=cfg)
+        effect2 = Effect(_effect2, cfg=cfg)
+
+        # then
+        assert (effect1 == effect2) is _result
+
+    #
+    # @pytest.mark.parametrize("_effect, _result", [
+    #     ([UBR(0, 15), UBR(0, 7)], 'OOOOOOOOOO|OOOOO.....')
+    # ])
+    # def test_should_visualize(self, _effect, _result, cfg):
+    #     assert repr(Effect(_effect, cfg=cfg)) == _result
diff --git a/tests/lcs/agents/racs/test_Mark.py b/tests/lcs/agents/racs/test_Mark.py
index 37922f49..e2d6fb9f 100644
--- a/tests/lcs/agents/racs/test_Mark.py
+++ b/tests/lcs/agents/racs/test_Mark.py
@@ -2,8 +2,7 @@
 
 from lcs import Perception
 from lcs.agents.racs import Configuration, Mark, Condition
-from lcs.representations import UBR
-from lcs.representations.RealValueEncoder import RealValueEncoder
+from lcs.representations import Interval
 
 
 class TestMark:
@@ -11,8 +10,7 @@ class TestMark:
     @pytest.fixture
     def cfg(self):
         return Configuration(classifier_length=2,
-                             number_of_possible_actions=2,
-                             encoder=RealValueEncoder(4))
+                             number_of_possible_actions=2)
 
     def test_should_initialize_empty_mark(self, cfg):
         # when
@@ -33,15 +31,15 @@ def test_should_detect_if_marked(self, cfg):
         mark = Mark(cfg)
 
         # when
-        mark[0].add(UBR(2, 5))
+        mark[0].add(Interval(.2, .2))
 
         # then
         assert mark.is_marked() is True
 
     @pytest.mark.parametrize("initmark, perception, changed", [
-        ([[], []], [0.5, 0.5], False),  # shouldn't set mark if empty
-        ([[8], []], [0.5, 0.5], False),  # encoded value already marked
-        ([[5], []], [0.5, 0.5], True)
+        ([[], []], [.5, .5], False),  # shouldn't set mark if not marked
+        ([[.5], []], [.5, .5], False),  # perception value already marked
+        ([[.2], []], [.5, .5], True)
     ])
     def test_should_complement_mark(self, initmark, perception, changed, cfg):
         # given
@@ -56,13 +54,13 @@ def test_should_complement_mark(self, initmark, perception, changed, cfg):
 
     @pytest.mark.parametrize("initmark, _p0, initcond, marked_count", [
         # not marked, all generic classifier, should mark two positions
-        ([[], []], [0.5, 0.5], [], 2),
+        ([[], []], [.5, .5], [], 2),
         # not marked, specified condition, shouldn't get marked
-        ([[], []], [0.5, 0.5], [UBR(1, 3), UBR(2, 3)], 0),
+        ([[], []], [.5, .5], [Interval(.1, .3), Interval(.2, .3)], 0),
         # not marked, one don't care, should mark one
-        ([[], []], [0.5, 0.5], [UBR(1, 3), UBR(0, 15)], 1),
+        ([[], []], [.5, .5], [Interval(.1, .3), Interval(0., 1.)], 1),
         # already marked, should use perception, one mark
-        ([[4], []], [0.5, 0.5], [], 1),
+        ([[.4], []], [.5, .5], [], 1),
     ])
     def test_should_set_mark_using_condition(self, initmark, _p0,
                                              initcond, marked_count, cfg):
@@ -91,11 +89,11 @@ def test_should_get_no_differences(self, cfg):
     @pytest.mark.parametrize("_m, _p0, _specif", [
         # There is no perception in mark - one attribute should be
         # randomly specified
-        ([[2], [4]], [.5, .5], 1),
+        ([[.2], [.4]], [.5, .5], 1),
         # One perception is marked - the other should be specified
-        ([[8], [4]], [.5, .5], 1),
+        ([[.5], [.2]], [.5, .5], 1),
         # Both perceptions are marked - no differences
-        ([[8], [8]], [.5, .5], 0)
+        ([[.5], [.5]], [.5, .5], 0)
     ])
     def test_should_handle_unique_differences(self, _m, _p0, _specif, cfg):
         # given
@@ -110,15 +108,15 @@ def test_should_handle_unique_differences(self, _m, _p0, _specif, cfg):
 
     @pytest.mark.parametrize("_m, _p0, _specificity", [
         # There are two marks in one attribute - it should be specified.
-        ([[1, 2], [4]], [.5, .5], 1),
+        ([[.1, .2], [.4]], [.5, .5], 1),
         # Here we have clear unique difference - specify it first
-        ([[1, 2], [8]], [.5, .5], 1),
+        ([[.1, .2], [.5]], [.5, .5], 1),
         # Two fuzzy attributes (containing perception value) - both
         # should be specified
-        ([[6, 8], [5, 8]], [.5, .5], 2),
+        ([[.4, .5], [.3, .5]], [.5, .5], 2),
         # Two fuzzy attributes - but one is unique (does not contain
         # perception)
-        ([[6, 8], [7, 9]], [.5, .5], 1),
+        ([[.4, .5], [.4, .6]], [.5, .5], 1),
     ])
     def test_should_handle_fuzzy_differences(self, _m, _p0, _specificity, cfg):
         # given
@@ -136,6 +134,7 @@ def _init_mark(vals, cfg):
         mark = Mark(cfg)
         for idx, attribs in enumerate(vals):
             for attrib in attribs:
+                assert type(attrib) is float
                 mark[idx].add(attrib)
 
         return mark
diff --git a/tests/lcs/agents/racs/test_metrics.py b/tests/lcs/agents/racs/test_metrics.py
index fccf0bf2..4887c89e 100644
--- a/tests/lcs/agents/racs/test_metrics.py
+++ b/tests/lcs/agents/racs/test_metrics.py
@@ -1,10 +1,9 @@
 import pytest
 
-from lcs.agents.racs import Configuration, ClassifierList, \
+from lcs.agents.racs import Configuration, ClassifiersList, \
     Classifier, Condition
 from lcs.agents.racs.metrics import count_averaged_regions
-from lcs.representations import UBR
-from lcs.representations.RealValueEncoder import RealValueEncoder
+from lcs.representations import Interval
 
 
 class TestMetrics:
@@ -12,20 +11,23 @@ class TestMetrics:
     @pytest.fixture
     def cfg(self):
         return Configuration(classifier_length=2,
-                             number_of_possible_actions=2,
-                             encoder=RealValueEncoder(4))
+                             number_of_possible_actions=2)
 
     def test_regions_averaging(self, cfg):
         # given
-        cl1 = Classifier(condition=Condition([UBR(2, 3), UBR(4, 5)], cfg),
-                         cfg=cfg)
-        cl2 = Classifier(condition=Condition([UBR(0, 3), UBR(4, 9)], cfg),
-                         cfg=cfg)
-        cl3 = Classifier(condition=Condition([UBR(1, 3), UBR(4, 15)], cfg),
-                         cfg=cfg)
-        cl4 = Classifier(condition=Condition([UBR(0, 13), UBR(0, 15)], cfg),
-                         cfg=cfg)
-        population = ClassifierList(*[cl1, cl2, cl3, cl4])
+        cl1 = Classifier(
+            condition=Condition([Interval(.2, .3), Interval(.4, .5)], cfg),
+            cfg=cfg)
+        cl2 = Classifier(
+            condition=Condition([Interval(0., .3), Interval(.4, .9)], cfg),
+            cfg=cfg)
+        cl3 = Classifier(
+            condition=Condition([Interval(.1, .3), Interval(.4, 1.)], cfg),
+            cfg=cfg)
+        cl4 = Classifier(
+            condition=Condition([Interval(0., .9), Interval(0., 1.)], cfg),
+            cfg=cfg)
+        population = ClassifiersList(*[cl1, cl2, cl3, cl4])
 
         # when
         result = count_averaged_regions(population)
diff --git a/tests/lcs/agents/test_PerceptionString.py b/tests/lcs/agents/test_PerceptionString.py
index 2190f3aa..2435960d 100644
--- a/tests/lcs/agents/test_PerceptionString.py
+++ b/tests/lcs/agents/test_PerceptionString.py
@@ -1,5 +1,5 @@
 from lcs.agents import PerceptionString
-from lcs.representations import UBR
+from lcs.representations import Interval
 
 
 class TestPerceptionString:
@@ -16,27 +16,27 @@ def test_should_create_empty_with_defaults(self):
         assert len(ps) == 3
         assert repr(ps) == '###'
 
-    def test_should_create_empty_for_ubr(self):
+    def test_should_create_empty_for_interval(self):
         # given
         length = 3
-        wildcard = UBR(0, 16)
+        wildcard = Interval(0., 1.)
 
         # when
-        ps = PerceptionString.empty(length, wildcard, oktypes=(UBR,))
+        ps = PerceptionString.empty(length, wildcard, oktypes=(Interval,))
 
         # then
         assert len(ps) == 3
         assert ps[0] == ps[1] == ps[2] == wildcard
         assert ps[0] is not ps[1] is not ps[2]
 
-    def test_should_safely_modify_single_attribute(self):
+    def test_should_safely_modify_single_boundary(self):
         # given
         length = 3
-        wildcard = UBR(0, 16)
-        ps = PerceptionString.empty(length, wildcard, oktypes=(UBR, ))
+        wildcard = Interval(0., 1.)
+        ps = PerceptionString.empty(length, wildcard, oktypes=(Interval, ))
 
         # when
-        ps[0].x1 = 2
+        ps[0].p = .5
 
         # then (check if objects are not stored using references)
-        assert ps[1].x1 == 0
+        assert ps[1].p == 0.
diff --git a/tests/lcs/representations/test_Interval.py b/tests/lcs/representations/test_Interval.py
new file mode 100644
index 00000000..8cba56ec
--- /dev/null
+++ b/tests/lcs/representations/test_Interval.py
@@ -0,0 +1,117 @@
+import pytest
+
+from lcs.representations import Interval
+
+
+class TestInterval:
+
+    def test_should_compare_without_ordering(self):
+        # given
+        i1 = Interval(0.0, 0.2)
+        i2 = Interval(0.2, 0.0)
+
+        # then
+        assert i1 == i2
+        assert (i1 != i2) is False
+
+    @pytest.mark.parametrize("items, expected_length", [
+        ([Interval(.2, .5), Interval(.5, .2)], 1),
+        ([Interval(.2, .5), Interval(.2, .5)], 1),
+        ([Interval(.2, .5), Interval(.2, .6)], 2)
+    ])
+    def test_should_not_allow_duplicates(self, items, expected_length):
+        # given
+        container = set(items)
+
+        # then
+        assert len(container) == expected_length
+
+    @pytest.mark.parametrize("p, q", [
+        (1.1, 0.2),
+        (0.2, 1.1),
+        (-0.1, 0.5),
+    ])
+    def test_should_not_initialize_due_to_wrong_range(self, p, q):
+        with pytest.raises(ValueError,
+                           match='Value [-]?.{4} not in range \[0, 1\]'):
+            Interval(p, q)
+
+    @pytest.mark.parametrize("p, q, left", [
+        (0.2, 0.5, 0.2),
+        (0.5, 0.2, 0.2),
+    ])
+    def test_should_determine_left_bound(self, p, q, left):
+        assert Interval(p, q).left == left
+
+    @pytest.mark.parametrize("p, q, right", [
+        (0.2, 0.5, 0.5),
+        (0.5, 0.2, 0.5),
+    ])
+    def test_should_determine_right_bound(self, p, q, right):
+        assert Interval(p, q).right == right
+
+    @pytest.mark.parametrize("_i, _span", [
+        (Interval(.0, 1.), 1),
+        (Interval(.5, .5), 0),
+        (Interval(.5, .6), .1),
+    ])
+    def test_should_calculate_bound_span(self, _i, _span):
+        assert abs(_i.span - _span) < 0.00001
+
+    @pytest.mark.parametrize("_i, _val, _result", [
+        (Interval(.5, .6), .1, Interval(.4, .6)),
+        (Interval(.7, .2), .1, Interval(.7, .1)),
+        (Interval(.4, .4), .1, Interval(.3, .4)),
+    ])
+    def test_should_add_to_left_bound(self, _i, _val, _result):
+        assert _i.add_to_left(_val) == _result
+
+    @pytest.mark.parametrize("_i, _val, _result", [
+        (Interval(.5, .6), .1, Interval(.5, .7)),
+        (Interval(.7, .2), .1, Interval(.8, .2)),
+        (Interval(.4, .4), .1, Interval(.4, .5)),
+    ])
+    def test_should_add_to_right_bound(self, _i, _val, _result):
+        assert _i.add_to_right(_val) == _result
+
+    @pytest.mark.parametrize("i1, i2, _result", [
+        (Interval(.0, 1.), Interval(.2, .4), True),
+        (Interval(.4, .6), Interval(.6, .7), False),
+        (Interval(.4, .6), Interval(.4, .5), True),
+        (Interval(.4, .4), Interval(.4, .4), True),
+        (Interval(.4, .4), Interval(.4, .5), False),
+        (Interval(.4, .5), Interval(.2, .7), False),
+        (Interval(.4, .6), Interval(.5, .7), False),
+    ])
+    def test_should_detect_incorporation(self, i1, i2, _result):
+        assert (i2 in i1) == _result
+
+    @pytest.mark.parametrize("_interval, _val", [
+        (Interval(0.2, 0.5), 0.3),
+        (Interval(0.53, 0.52), 0.525),
+        (Interval(0., 1.), 0.),
+    ])
+    def test_should_detect_value_in_interval(self, _interval, _val):
+        assert _val in _interval
+
+    @pytest.mark.parametrize("_i1, _i2, _the_same", [
+        (Interval(.1, .2), Interval(.1, .2), True),
+        (Interval(.1, .2), Interval(.2, .3), False),
+        (Interval(.1, .2), Interval(.101, .201), True),
+    ])
+    def test_should_compare_intervals(self, _i1, _i2, _the_same):
+        assert (_i1 == _i2) == _the_same
+
+    @pytest.mark.parametrize("_interval, _val", [
+        (Interval(0.2, 0.5), 0.5),
+        (Interval(0.53, 0.52), 0.53),
+        (Interval(0., 1.), 1.),
+    ])
+    def test_should_not_be_strict_on_right_boundary(self, _interval, _val):
+        assert _val not in _interval
+
+    @pytest.mark.parametrize("_interval, _repr", [
+        (Interval(.2412, .5512), "[0.24;0.55]")
+    ])
+    def test_representation(self, _interval, _repr):
+        assert str(_interval) == _repr
diff --git a/tests/lcs/representations/test_UBR.py b/tests/lcs/representations/test_UBR.py
deleted file mode 100644
index 028cbe70..00000000
--- a/tests/lcs/representations/test_UBR.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import pytest
-
-from lcs.representations import UBR
-
-
-class TestUBR:
-
-    def test_should_compare_without_ordering(self):
-        # given
-        o1 = UBR(0, 2)
-        o2 = UBR(2, 0)
-
-        # then
-        assert o1 == o2
-        assert (o1 != o2) is False
-
-    @pytest.mark.parametrize("items, expected_length", [
-        ([UBR(2, 5), UBR(5, 2)], 1),
-        ([UBR(2, 5), UBR(2, 5)], 1),
-        ([UBR(2, 5), UBR(2, 6)], 2)
-    ])
-    def test_should_not_allow_duplicates(self, items, expected_length):
-        # given
-        container = set(items)
-
-        # then
-        assert len(container) == expected_length
-
-    @pytest.mark.parametrize("_ubr, _span", [
-        (UBR(0, 15), 16),
-        (UBR(5, 5), 1),
-        (UBR(5, 6), 2),
-    ])
-    def test_should_calculate_bound_span(self, _ubr, _span):
-        assert _ubr.bound_span == _span
-
-    @pytest.mark.parametrize("ubr1, ubr2, _result", [
-        (UBR(0, 15), UBR(2, 4), True),
-        (UBR(4, 6), UBR(6, 7), False),
-        (UBR(4, 6), UBR(4, 5), True),
-        (UBR(4, 4), UBR(4, 4), True),
-        (UBR(4, 4), UBR(4, 5), False),
-        (UBR(4, 5), UBR(2, 7), False),
-        (UBR(4, 6), UBR(5, 7), False),
-    ])
-    def test_should_detect_incorporation(self, ubr1, ubr2, _result):
-        assert ubr1.incorporates(ubr2) == _result
diff --git a/tests/lcs/representations/test_realvalueencoder.py b/tests/lcs/representations/test_realvalueencoder.py
deleted file mode 100644
index 673b3a8a..00000000
--- a/tests/lcs/representations/test_realvalueencoder.py
+++ /dev/null
@@ -1,130 +0,0 @@
-import random
-
-import numpy as np
-import pytest
-
-from lcs.representations.RealValueEncoder import RealValueEncoder
-
-
-class TestRealValueEncoder:
-
-    def test_should_deny_encoding_illegal_values(self):
-        # given
-        encoder = RealValueEncoder(2)
-
-        # when
-        with pytest.raises(ValueError) as e1:
-            encoder.encode(-0.2)
-
-        with pytest.raises(ValueError) as e2:
-            encoder.encode(1.1)
-
-        # then
-        assert e1 is not None
-        assert e2 is not None
-
-    def test_should_deny_decoding_illegal_values(self):
-        # given
-        encoder = RealValueEncoder(2)
-
-        # when
-        with pytest.raises(ValueError) as e1:
-            encoder.decode(-1)
-
-        with pytest.raises(ValueError) as e2:
-            encoder.decode(5)
-
-        # then
-        assert e1 is not None
-        assert e2 is not None
-
-    @pytest.mark.parametrize("_bits, _val, _encoded", [
-        (1, .0, 0), (1, .5, 1), (1, 1., 1),
-        (2, .0, 0), (2, .25, 1), (2, .5, 2), (2, .75, 3), (2, 1., 3),
-        (3, .0, 0), (3, .5, 4), (3, 1., 7),
-    ])
-    def test_should_encode(self, _bits, _val, _encoded):
-        assert RealValueEncoder(_bits).encode(_val) == _encoded
-
-    def test_should_decode_values(self):
-        # given
-        bits = 4
-        encoder = RealValueEncoder(bits)
-
-        # then
-        assert 0.0 == encoder.decode(0)
-        assert abs(0.5 - encoder.decode(8)) < 0.05
-        assert 1.0 == encoder.decode(15)
-
-    def test_should_encode_and_decode_approximately(self):
-        # given
-        encoder = RealValueEncoder(8)
-        epsilon = 0.01
-        observation = random.random()
-
-        # when
-        encoded = encoder.encode(observation)
-        decoded = encoder.decode(encoded)
-
-        # then
-        assert abs(observation - decoded) < epsilon
-
-    @pytest.mark.parametrize("_bits, _min_range, _max_range", [
-        (1, 0, 1),
-        (2, 0, 3),
-        (4, 0, 15),
-        (8, 0, 255)
-    ])
-    def test_should_return_min_max_range(self, _bits, _min_range, _max_range):
-        # given
-        encoder = RealValueEncoder(_bits)
-
-        # when
-        min_val, max_val = encoder.range
-
-        # then
-        assert min_val == _min_range
-        assert max_val == _max_range
-
-    @pytest.mark.parametrize("_p", [
-        0.8, np.float_(0.2), 0
-    ])
-    def test_encode_should_return_integer(self, _p):
-        # given
-        encoder = RealValueEncoder(2)
-
-        # when
-        encoded = encoder.encode(_p)
-
-        # then
-        assert type(encoded) is int
-
-    def test_should_encode_with_noise_added(self):
-        # given
-        encoder = RealValueEncoder(16)
-        noise_max = 0.1
-
-        for _ in range(50):
-            # when
-            val = random.random()
-            encoded = encoder.encode(val)
-            encoded_with_noise = encoder.encode(val, noise_max)
-
-            # then
-            assert encoded_with_noise <= encoder.range[1]
-            assert encoded_with_noise >= encoded
-
-    def test_should_encode_with_noise_subtracted(self):
-        # given
-        encoder = RealValueEncoder(16)
-        noise_max = -0.1
-
-        for _ in range(50):
-            # when
-            val = random.random()
-            encoded = encoder.encode(val)
-            encoded_with_noise = encoder.encode(val, noise_max)
-
-            # then
-            assert encoded_with_noise >= encoder.range[0]
-            assert encoded_with_noise <= encoded
diff --git a/tests/lcs/strategies/test_subsumption.py b/tests/lcs/strategies/test_subsumption.py
index fcd03e2d..9ca7168a 100644
--- a/tests/lcs/strategies/test_subsumption.py
+++ b/tests/lcs/strategies/test_subsumption.py
@@ -5,8 +5,7 @@
 import lcs.agents.acs2 as acs2
 import lcs.agents.racs as racs
 from lcs.agents.racs import Effect, Condition
-from lcs.representations import UBR
-from lcs.representations.RealValueEncoder import RealValueEncoder
+from lcs.representations import Interval
 from lcs.strategies.subsumption import find_subsumers, \
     is_subsumer, does_subsume
 
@@ -37,8 +36,7 @@ def acs2_cfg(self):
     @pytest.fixture
     def racs_cfg(self):
         return racs.Configuration(classifier_length=2,
-                                  number_of_possible_actions=2,
-                                  encoder=RealValueEncoder(4))
+                                  number_of_possible_actions=2)
 
     def test_should_find_subsumer(self, acs2_cfg):
         # given
@@ -336,20 +334,27 @@ def test_should_subsume_another_classifier(
     @pytest.mark.parametrize(
         "_e1, _e2, _exp1, _marked, _reliable,"
         "_more_general, _condition_matching, _result", [
-            ([UBR(2, 4), UBR(5, 6)], [UBR(2, 4), UBR(5, 6)], 30, False, True,
-             True, True, True),  # all good
-            ([UBR(2, 4), UBR(5, 6)], [UBR(2, 4), UBR(5, 6)], 30, False, False,
-             True, True, False),  # not reliable
-            ([UBR(2, 4), UBR(5, 6)], [UBR(2, 4), UBR(5, 6)], 30, True, True,
-             True, True, False),  # marked
-            ([UBR(2, 4), UBR(5, 6)], [UBR(2, 4), UBR(5, 6)], 30, False, True,
-             False, True, False),  # less general
-            ([UBR(2, 4), UBR(5, 6)], [UBR(2, 4), UBR(5, 6)], 30, False, True,
-             True, False, False),  # condition not matching
-            ([UBR(2, 4), UBR(5, 6)], [UBR(2, 4), UBR(5, 7)], 30, False, True,
-             True, True, False),  # different effects
-            ([UBR(2, 4), UBR(5, 6)], [UBR(2, 4), UBR(5, 7)], 10, False, True,
-             True, True, False),  # not experienced
+            ([Interval(.2, .4), Interval(.5, .6)],
+             [Interval(.2, .4), Interval(.5, .6)],
+             30, False, True, True, True, True),  # all good
+            ([Interval(.2, .4), Interval(.5, .6)],
+             [Interval(.2, .4), Interval(.5, .6)],
+             30, False, False, True, True, False),  # not reliable
+            ([Interval(.2, .4), Interval(.5, .6)],
+             [Interval(.2, .4), Interval(.5, .6)],
+             30, True, True, True, True, False),  # marked
+            ([Interval(.2, .4), Interval(.5, .6)],
+             [Interval(.2, .4), Interval(.5, .6)],
+             30, False, True, False, True, False),  # less general
+            ([Interval(.2, .4), Interval(.5, .6)],
+             [Interval(.2, .4), Interval(.5, .6)],
+             30, False, True, True, False, False),  # condition not matching
+            ([Interval(.2, .4), Interval(.5, .6)],
+             [Interval(.2, .4), Interval(.5, .7)],
+             30, False, True, True, True, False),  # different effects
+            ([Interval(.2, .4), Interval(.5, .6)],
+             [Interval(.2, .4), Interval(.5, .6)],
+             10, False, True, True, True, False),  # not experienced
         ])
     def test_should_detect_subsumption(self, _e1, _e2, _exp1, _marked,
                                        _reliable, _more_general,