fixed docs refs and kw parameter for older Julia versions

Author: wildart

docs/src/crossover.md

@@ -76,4 +76,4 @@ Evolutionary.crosstree
 
 [^5]: K. Deep, K. P. Singh, M. L. Kansal, and C. Mohan, "A real coded  genetic algorithm for solving integer and mixed integer optimization problems.", Appl. Math. Comput. 212, 505-518, 2009
 
-[^6]  K. Deb, R. B. Agrawal, "Simulated Binary Crossover for Continuous Search Space", Complex Syst., 9., 1995
+[^6]: K. Deb, R. B. Agrawal, "Simulated Binary Crossover for Continuous Search Space", Complex Syst., 9., 1995

docs/src/mutation.md

@@ -92,4 +92,4 @@ shrink
 
 [^8]: P. J. Angeline, "An investigation into the sensitivity of genetic programming to the frequency of leaf selection during subtree crossover", Genetic Programming 1996: Proceedings of the First Annual Conference, 21–29, 1996.
 
-[^9]  K. Deb, R. B. Agrawal, "Simulated Binary Crossover for Continuous Search Space", Complex Syst., 9., 1995
+[^9]: K. Deb, R. B. Agrawal, "Simulated Binary Crossover for Continuous Search Space", Complex Syst., 9., 1995

src/mutations.jl

@@ -250,7 +250,7 @@ function PLM(Δ::Vector, η=2; pm::Real=NaN) # index of distribution p
     end
     return mutation
 end
-PLM(lower::Vector, upper::Vector, η::Real = 2; pm::Real=NaN) = PLM(upper-lower, η; pm)
+PLM(lower::Vector, upper::Vector, η::Real = 2; pm::Real=NaN) = PLM(upper-lower, η; pm=pm)
 
 
 # Combinatorial mutations (applicable to binary vectors)

src/recombinations.jl

@@ -288,11 +288,12 @@ function MILX(μ::Real = 0.0, b_real::Real = 0.15, b_int::Real = 0.35) # locatio
 end
 
 """
-    LX(μ::Real = 0.0, b::Real = 0.2)
+    SBX(pm::Real = 0.5, η::Integer = 2)
 
-Returns a Simulated Binary Crossover (SBX) recombination operation[^6], see [Recombination Interface](@ref).
+Returns a Simulated Binary Crossover (SBX) recombination operation, see [Recombination Interface](@ref),
+with the mutation probability `pm` of the recombinant component, and is the crossover distribution index `η`[^6].
 """
-function SBX(p::Real = 0.5, η::Integer = 2)
+function SBX(pm::Real = 0.5, η::Integer = 2)
     function sbxv(v1::T, v2::T; rng::AbstractRNG=Random.GLOBAL_RNG) where {T <: AbstractVector}
         n = length(v1)
         u = rand(rng, n)
@@ -304,7 +305,7 @@ function SBX(p::Real = 0.5, η::Integer = 2)
         μ = (v1 + v2)./2
         diff = v1 - v2
         c = β.*diff./2
-        mask_set = n == 1 ? [1] : rand(rng, n) .<= p
+        mask_set = n == 1 ? [1] : rand(rng, n) .<= pm
         c1 = copy(μ) # c2 = μ - c
         c1[mask_set] .-= c[mask_set]
         c2 = copy(μ) # c2 = μ + c