Subset crossover (#89)

* added binary subset crossover (BSX)

Co-authored-by: Art <[email protected]>

Author: miguelbiron

README.md

@@ -60,6 +60,7 @@ a
     - UX (uniform)
     - BINX (binary)
     - EXPX (exponential)
+    - BSX (binary subset)
   - real valued
     - DC (discrete)
     - AX (average)

docs/src/crossover.md

@@ -32,6 +32,7 @@ SHFX
 UX
 BINX
 EXPX
+BSX
 ```
 
 Real-valued crossovers:
@@ -78,3 +79,5 @@ 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
+
+[^7]: M. A. Wolters, “A Genetic Algorithm for Selection of Fixed-Size Subsets with Application to Design Problems”, J. Stat. Soft., vol. 68, no. 1, pp. 1–18, Nov. 2015.

src/Evolutionary.jl

@@ -24,6 +24,7 @@ module Evolutionary
            SPX, TPX, UX, SHFX,
            DC, AX, WAX, IC, LC, HX, LX, MILX, SBX,
            PMX, OX1, CX, OX2, POS,
+           BSX,
            # GA selections
            ranklinear, uniformranking, roulette, rouletteinv, sus, susinv,
            tournament, truncation,

src/recombinations.jl

@@ -180,6 +180,26 @@ function EXPX(Cr::Real = 0.5)
     return expxvr
 end
 
+"""
+    BSX(k::Int)
+
+Binary Subset Crossover[^7]. Produces two offsprings by first pooling the unique
+items of the two parents, and then creating each offspring by sampling without 
+replacement at most `k` elements from the pool of items.
+"""
+function BSX(k::Int)
+    function BSX(v1::T, v2::T; rng::AbstractRNG=Random.GLOBAL_RNG) where {T <: AbstractVector{Bool}}
+        l = length(v1) # get number of available elements
+        pooled = findall(v1 .| v2) # pool parents selections
+        K = min(k,length(pooled)) # cannot sample more than the items in pool
+        c1 = falses(l) # init child 1
+        c2 = falses(l) # init child 2
+        c1[shuffle(rng, pooled)[1:K]] .= true # fill child 1 with sample from pool w/o replacement
+        c2[shuffle(rng, pooled)[1:K]] .= true # fill child 2 with sample from pool w/o replacement
+        return c1, c2
+    end
+    return BSX
+end
 
 # Real valued crossovers
 # ----------------------
@@ -544,6 +564,8 @@ function POS(v1::T, v2::T; rng::AbstractRNG=Random.GLOBAL_RNG) where {T <: Abstr
     return c1,c2
 end
 
+
+
 # ===================
 # Genetic Programming
 # ===================

test/recombinations.jl

@@ -52,7 +52,13 @@
         @test OX2([1:8;], [2,4,6,8,7,5,3,1], rng=rng) == ([1,2,3,4,6,5,7,8], [2,4,3,8,7,5,6,1])
         Random.seed!(rng, 18)
         @test POS([1:8;], [2,4,6,8,7,5,3,1], rng=rng) == ([1,4,6,2,3,5,7,8], [4,2,3,8,7,6,5,1])
-
+        
+        xo = BSX(3)                                                                 
+        @testset "BSX" for i in 1:100
+            off1, off2 = xo(Bool[1,0,1,0,1,0], Bool[1,1,1,1,0,0])
+            @test off1[end] == 0 && off2[end] == 0
+            @test sum(off1) == 3 && sum(off2) == 3
+        end
     end
 
     @testset "DE" begin