Simplify uniformranking

Author: aahaselgrove

src/Evolutionary.jl

@@ -12,7 +12,7 @@ using Random
            discrete, waverage, intermediate, line,
            pmx, ox1, cx, ox2, pos,
            # GA selections
-           ranklinear, uniformranking, roulette, sus, tournament, truncation,
+           ranklinear, rankuniform, roulette, sus, tournament, truncation,
            # Optimization methods
            es, cmaes, ga
 

src/selections.jl

@@ -8,7 +8,7 @@ function ranklinear(sp::Float64)
     function rank(fitness::Vector{<:Real}, N::Int)
         λ = length(fitness)
         rank = sortperm(fitness)
-
+        
         prob = Vector{Float64}(undef, λ)
         for i in 1:λ
             prob[i] = ( 2.0- sp + 2.0*(sp - 1.0)*(rank[i] - 1.0) / (λ - 1.0) ) / λ
@@ -20,15 +20,16 @@ function ranklinear(sp::Float64)
 end
 
 # (μ, λ)-uniform ranking selection
-function uniformranking(μ::Int)
-    function uniformrank(fitness::Vector{<:Real}, N::Int)
-        λ = length(fitness)
-        @assert μ < λ "μ should equal $(λ)"
+function rankuniform(fitness::Vector{<:Real}, N::Int)
+    μ = N
+    λ = length(fitness)
+    @assert μ <= λ "μ must be ≤ λ = $(λ)"
+    
+    prob = zeros(λ)
+    rank = sortperm(fitness, rev=true)
+    prob[rank[1:μ]] .= 1/μ
 
-        prob = fill(1/μ, μ)
-        return pselection(prob, N)
-    end
-    return uniformrank
+    return pselection(prob, N)
 end
 
 # Roulette wheel (proportionate selection) selection

test/selections.jl

@@ -12,10 +12,9 @@
 
     @testset "Uniform" begin
         Random.seed!(2);
-        s = uniformranking(2)
-        @test s([1.0,2.0,3.0], 2) == [2,3]
-        @test s([1,2,3], 2) == [2,3]
-        @test_throws AssertionError s([1.,2.], 2)
+        @test rankuniform([1.0,2.0,3.0], 2) == [2,3]
+        @test rankuniform([1,2,3], 2) == [2,3]
+        @test_throws AssertionError rankuniform([1.,2.], 3)
     end
 
     @testset "Roulette" begin