config_mcts_memory.yaml

# Configuration for MCTS Strategy - Real-Time Adaptive Signal Processing
# MCTS with Memory enabled
# Resource usage: 25 iterations × expansion_width=4 = ~100 LLM calls

# Search strategy
search_strategy: "mcts"
expansion_width: 4             # Generate N children per expansion
exploration_constant: 1.414    # UCT exploration parameter (√2)

# Evolution settings
max_iterations: 100     # Adjusted for comparable LLM usage (~100 calls)
checkpoint_interval: 5
log_level: "INFO"

# Memory Configuration
memory:
  enabled: true  # Enable memory-augmented evolution
  load_from_snapshot: false  # Start fresh - no existing snapshot to load
  semantic_search_topk: 3  # Number of similar parents to find for semantic search
  embed_model: "text-embedding-3-large"  # OpenAI embedding model for semantic search

# LLM configuration
llm:
  primary_model: "gpt-5-mini"
  primary_model_weight: 0.8
  secondary_model: "gpt-5-nano"
  secondary_model_weight: 0.2
  api_base: "https://api.openai.com/v1"
  temperature: 0.6
  top_p: 0.95
  max_tokens: 32000
  timeout: 180  # Increased timeout for memory-enabled runs with embeddings

# Prompt configuration
prompt:
  system_message: "You are an expert signal processing engineer specializing in real-time adaptive filtering algorithms. Your task is to improve a signal processing algorithm that filters volatile, non-stationary time series data using a sliding window approach. The algorithm must minimize noise while preserving signal dynamics with minimal computational latency and phase delay. Focus on the multi-objective optimization of: (1) Slope change minimization - reducing spurious directional reversals, (2) Lag error minimization - maintaining responsiveness, (3) Tracking accuracy - preserving genuine signal trends, and (4) False reversal penalty - avoiding noise-induced trend changes. Consider advanced techniques like adaptive filtering (Kalman filters, particle filters), multi-scale processing (wavelets, EMD), predictive enhancement (polynomial fitting, neural networks), and trend detection methods."
  num_top_programs: 4
  num_diverse_programs: 2
  use_template_stochasticity: true

# Database configuration
database:
  population_size: 80
  archive_size: 30
  elite_selection_ratio: 0.15
  exploitation_ratio: 0.65
  feature_dimensions: []  # No MAP-Elites features for MCTS

# Evaluator configuration
evaluator:
  timeout: 120
  cascade_evaluation: true
  cascade_thresholds: [0.3, 0.6]
  parallel_evaluations: 4
  use_llm_feedback: false

# Evolution settings
diff_based_evolution: true
max_code_length: 60000