Refactor crossover operators

jMetal · Jun 25, 2024 · 110de54 · 110de54
1 parent c410616
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Showing 17 changed files with 1,994 additions and 2,253 deletions.
diff --git a/examples/MIC2024Example.NSGAIISolvingIntegerProblem.jl b/examples/MIC2024Example.NSGAIISolvingIntegerProblem.jl
@@ -30,7 +30,7 @@ solver::NSGAII = NSGAII(
     termination = TerminationByEvaluations(2500))
 
 solver.mutation = PolynomialMutation(1.0 / numberOfVariables(problem), 20.0, problem.bounds)
-solver.crossover = SBXCrossover(1.0, 20.0, problem.bounds)
+solver.crossover = SBXCrossover(probability = 1.0, distributionIndex = 20.0, bounds = problem.bounds)
 
 # Step 3: optimize
 optimize(solver)

diff --git a/examples/NSGAIIAsAnEvolutionaryAlgorithm.jl b/examples/NSGAIIAsAnEvolutionaryAlgorithm.jl
@@ -17,11 +17,11 @@ function main()
     solver.termination = TerminationByEvaluations(25000)
 
     mutation = PolynomialMutation(1.0 / numberOfVariables(problem), 20.0, problem.bounds)
-    crossover = SBXCrossover(0.9, 20.0, problem.bounds)
+    crossover = SBXCrossover(probability = 0.9, distributionIndex = 20.0, bounds = problem.bounds)
 
     """
     mutation = UniformMutation(1.0/numberOfVariables(problem), 20.0, problem.bounds)
-    crossover = BLXAlphaCrossover(1.0, 0.5, problem.bounds)
+    crossover = BLXAlphaCrossover(probability = 1.0, alpha = 0.5, bounds = problem.bounds)
     """
 
     solver.variation = CrossoverAndMutationVariation(offspringPopulationSize, crossover, mutation)

diff --git a/examples/NSGAIIBinaryProblemExplicitSettings.jl b/examples/NSGAIIBinaryProblemExplicitSettings.jl
@@ -9,7 +9,7 @@ function main()
         populationSize = 50, 
         termination = TerminationByEvaluations(2000),
         mutation = BitFlipMutation(1.0 / problem.numberOfBits),
-        crossover = SinglePointCrossover(1.0)
+        crossover = SinglePointCrossover(probability = 1.0)
         )
 
     optimize(solver)

diff --git a/examples/NSGAIIFloatProblemExplicitSettings.jl b/examples/NSGAIIFloatProblemExplicitSettings.jl
@@ -9,7 +9,7 @@ function main()
         problem,
         populationSize = 50, 
         termination = TerminationByComputingTime(Dates.Second(2)),
-        crossover = SBXCrossover(1.0, 20.0, problem.bounds),
+        crossover = SBXCrossover(probability = 1.0, distributionIndex = 20.0, bounds = problem.bounds),
         mutation = PolynomialMutation(1.0 / numberOfVariables(problem), 20.0, problem.bounds))
 
     optimize(solver)

diff --git a/examples/NSGAIISolvingAConstrainedProblem.jl b/examples/NSGAIISolvingAConstrainedProblem.jl
@@ -13,7 +13,7 @@ function main()
     solver.termination = TerminationByEvaluations(25000)
 
     solver.mutation = PolynomialMutation(1.0 / numberOfVariables(problem), 20.0, problem.bounds)
-    solver.crossover = SBXCrossover(1.0, 20.0, problem.bounds)
+    solver.crossover = SBXCrossover(probability = 1.0, distributionIndex = 20.0, bounds = problem.bounds)
 
     solver.dominanceComparator = ConstraintsAndDominanceComparator()
 

diff --git a/examples/NSGAIIWithExternalCrowdingDistanceArchive.jl b/examples/NSGAIIWithExternalCrowdingDistanceArchive.jl
@@ -22,7 +22,7 @@ function main()
 
     mutation = PolynomialMutation(1.0 / numberOfVariables(problem), 20.0, problem.bounds)
 
-    crossover = SBXCrossover(0.9, 20.0, problem.bounds)
+    crossover = SBXCrossover(probability = 0.9, distributionIndex = 20.0, bounds = problem.bounds)
 
     solver.variation = CrossoverAndMutationVariation(offspringPopulationSize, crossover, mutation)
 

diff --git a/examples/NSGAIIWithExternalUnboundedArchive.jl b/examples/NSGAIIWithExternalUnboundedArchive.jl
@@ -19,7 +19,7 @@ function main()
 
     mutation = PolynomialMutation(1.0 / numberOfVariables(problem), 20.0, problem.bounds)
 
-    crossover = SBXCrossover(0.9, 20.0, problem.bounds)
+    crossover = SBXCrossover(probability = 0.9, distributionIndex = 20.0, bounds = problem.bounds)
 
     solver.variation = CrossoverAndMutationVariation(offspringPopulationSize, crossover, mutation)
 

diff --git a/examples/singleObjectiveGeneticAlgorithmOneMax.jl b/examples/singleObjectiveGeneticAlgorithmOneMax.jl
@@ -16,7 +16,7 @@ function main()
     solver.termination = TerminationByEvaluations(40000)
 
     mutation = BitFlipMutation(1.0 / numberOfVariables(problem))
-    crossover = SinglePointCrossover(1.0)
+    crossover = SinglePointCrossover(probability = 1.0)
     solver.variation = CrossoverAndMutationVariation(offspringPopulationSize, crossover, mutation)
 
     solver.selection = BinaryTournamentSelection(solver.variation.matingPoolSize, IthObjectiveComparator(1))

diff --git a/examples/singleObjectiveGeneticAlgorithmSphere.jl b/examples/singleObjectiveGeneticAlgorithmSphere.jl
@@ -17,10 +17,8 @@ function main()
     solver.termination = TerminationByEvaluations(700000)
 
     mutation = PolynomialMutation(1.0 / numberOfVariables(problem), 20.0, problem.bounds)
-    """
-    solver.crossover = BLXAlphaCrossover((probability=1.0, alpha=0.5, bounds=problem.bounds))
-    """
-    crossover = SBXCrossover(1.0, 20.0, problem.bounds)
+
+    crossover = SBXCrossover(probability = 1.0, distributionIndex = 20.0, bounds = problem.bounds)
     solver.variation = CrossoverAndMutationVariation(offspringPopulationSize, crossover, mutation)
 
     solver.selection = BinaryTournamentSelection(solver.variation.matingPoolSize, IthObjectiveComparator(1))

diff --git a/examples/singleObjectiveGeneticAlgorithmTerminatingByComputingTime.jl b/examples/singleObjectiveGeneticAlgorithmTerminatingByComputingTime.jl
@@ -18,9 +18,9 @@ function main()
 
     mutation = PolynomialMutation(1.0 / numberOfVariables(problem), 20.0, problem.bounds)
     """
-    solver.crossover = BLXAlphaCrossover((probability=1.0, alpha=0.5, bounds=problem.bounds))
+    solver.crossover = BLXAlphaCrossover(probability=1.0, alpha=0.5, bounds=problem.bounds)
     """
-    crossover = SBXCrossover(1.0, 20.0, problem.bounds)
+    crossover = SBXCrossover(probability = 1.0, distributionIndex = 20.0, bounds = problem.bounds)
     solver.variation = CrossoverAndMutationVariation(offspringPopulationSize, crossover, mutation)
 
     solver.selection = BinaryTournamentSelection(solver.variation.matingPoolSize, IthObjectiveComparator(1))

diff --git a/notebooks/Dealing with constrained problems.ipynb b/notebooks/Dealing with constrained problems.ipynb
diff --git a/notebooks/NSGA-II notebook.ipynb b/notebooks/NSGA-II notebook.ipynb
diff --git a/notebooks/NSGA-II with external archive.ipynb b/notebooks/NSGA-II with external archive.ipynb
diff --git a/src/algorithm/NSGAII.jl b/src/algorithm/NSGAII.jl
@@ -16,7 +16,7 @@ mutable struct NSGAII <: Algorithm
     problem :: ContinuousProblem; 
     populationSize = 100, 
     termination = TerminationByEvaluations(25000), dominanceComparator = numberOfConstraints(problem) == 0 ? DefaultDominanceComparator() : ConstraintsAndDominanceComparator(),
-    crossover = SBXCrossover(1.0, 20.0, problem.bounds),
+    crossover = SBXCrossover(probability = 1.0, distributionIndex = 20.0, bounds = problem.bounds),
     mutation = PolynomialMutation(1.0/numberOfVariables(problem), 20.0, problem.bounds))
     algorithm = new()
     algorithm.solver = EvolutionaryAlgorithm()
@@ -35,7 +35,7 @@ mutable struct NSGAII <: Algorithm
     problem :: BinaryProblem; 
     populationSize = 100, 
     termination = TerminationByEvaluations(25000), dominanceComparator = numberOfConstraints(problem) == 0 ? DefaultDominanceComparator() : ConstraintsAndDominanceComparator(),
-    crossover = SinglePointCrossover(1.0),
+    crossover = SinglePointCrossover(probability = 1.0),
     mutation = BitFlipMutation(1.0/problem.numberOfBits))
     algorithm = new()
     algorithm.solver = EvolutionaryAlgorithm()

diff --git a/src/operator/crossover.jl b/src/operator/crossover.jl
@@ -7,6 +7,8 @@ struct BLXAlphaCrossover <: CrossoverOperator
 
   function BLXAlphaCrossover(;probability = 0.9, alpha = 0.5, bounds = [])
     @assert bounds != [] "The bounds list is empty"
+    @assert probability >= 0.0 string("The probability ", probability, " must be equal or greater than zero")
+    @assert alpha >= 0 string("The alpha value ", alpha, " cannot be negative")
 
     return new(probability, alpha, bounds)
   end
@@ -58,6 +60,8 @@ struct SBXCrossover <: CrossoverOperator
 
   function SBXCrossover(;probability = 0.9, distributionIndex = 20.0, bounds = [])
     @assert bounds != [] "The bounds list is empty"
+    @assert probability >= 0.0 string("The probability ", probability, " must be equal or greater than zero")
+    @assert distributionIndex >= 0 string("The distributionIndex value ", alpha, " cannot be negative")
 
     return new(probability, distributionIndex, bounds)
   end
@@ -220,6 +224,13 @@ end
 # Single point crossover
 struct SinglePointCrossover <: CrossoverOperator
   probability::Real 
+
+  function SinglePointCrossover(;probability = 0.9)
+    @assert probability >= 0.0 string("The probability ", probability, " must be equal or greater than zero")
+
+    return new(probability)
+  end
+
 end
 
 function numberOfRequiredParents(crossover::SinglePointCrossover)

diff --git a/test/component/evolutionaryAlgorithm/variationTest.jl b/test/component/evolutionaryAlgorithm/variationTest.jl
@@ -5,7 +5,7 @@ function crossoverAndMutationVariationIsCorrectlyInitialized()
     solutionBounds = [Bounds{Float64}(1.0, 10.0), Bounds{Float64}(1.0, 10.0)]
 
     mutation = PolynomialMutation(0.01, 20.0, solutionBounds)
-    crossover = SBXCrossover(0.9, 30.0, solutionBounds)
+    crossover = SBXCrossover(probability = 0.9, distributionIndex = 30.0, bounds = solutionBounds)
 
     variation = CrossoverAndMutationVariation(offspringPopulationSize, crossover, mutation)
 
@@ -22,7 +22,7 @@ function theMatingPoolSizeWhenTheOffspringPopulationSizeIsOneMustBeTwo()
     solutionBounds = [Bounds{Float64}(1.0, 10.0), Bounds{Float64}(1.0, 10.0)]
 
     mutation = PolynomialMutation(0.01, 20.0, solutionBounds)
-    crossover = SBXCrossover(0.9, 30.0, solutionBounds)
+    crossover = SBXCrossover(probability = 0.9, distributionIndex = 30.0, bounds = solutionBounds)
 
     variation = CrossoverAndMutationVariation(offspringPopulationSize, crossover, mutation)
 
@@ -39,7 +39,7 @@ function crossoverAndMutationVariationReturnTheCorrectNumberOfSolutionsCaseA()
     solutionBounds = matingPool[1].bounds
 
     mutation = PolynomialMutation(0.01, 20.0, solutionBounds)
-    crossover = SBXCrossover(0.9, 30.0, solutionBounds)
+    crossover = SBXCrossover(probability = 0.9, distributionIndex = 30.0, bounds = solutionBounds)
 
     variation = CrossoverAndMutationVariation(offspringPopulationSize, crossover, mutation)
 
@@ -56,7 +56,7 @@ function crossoverAndMutationVariationReturnTheCorrectNumberOfSolutionsCaseB()
     solutionBounds = matingPool[1].bounds
 
     mutation = PolynomialMutation(0.01, 20.0, solutionBounds)
-    crossover = SBXCrossover(0.9, 30.0, solutionBounds)
+    crossover = SBXCrossover(probability = 0.9, distributionIndex = 30.0, bounds = solutionBounds)
 
     variation = CrossoverAndMutationVariation(offspringPopulationSize, crossover, mutation)
 

diff --git a/test/operator/crossoverTest.jl b/test/operator/crossoverTest.jl
@@ -6,7 +6,7 @@ function singlePointCrossoverWithProbabilityZeroReturnTwoSolutionsEqualToThePare
   parent1 = BinarySolution(initBitVector("101000"), [], [], Dict())
   parent2 = BinarySolution(initBitVector("010011"), [], [], Dict())
 
-  crossover = SinglePointCrossover(0.0)
+  crossover = SinglePointCrossover(probability = 0.0)
   children = recombine(parent1, parent2, crossover)
 
   return isequal(parent1, children[1]) && isequal(parent2, children[2])
@@ -16,16 +16,16 @@ function singlePointCrossoverWithWithProbabilityOneChangesAllTheVariableValuesIn
   parent1 = BinarySolution(initBitVector("101000"), [], [], Dict())
   parent2 = BinarySolution(initBitVector("010011"), [], [], Dict())
 
-  crossover = SinglePointCrossover(1.0)
+  crossover = SinglePointCrossover(probability = 1.0)
   children = recombine(parent1, parent2, crossover)
 
   return !isequal(parent1, children[1]) && !isequal(parent2, children[2])
 end
 
 @testset "Single point crossover tests" begin
-  @test SinglePointCrossover(0.1).probability == 0.1
-  @test numberOfRequiredParents(SinglePointCrossover(0.1)) == 2
-  @test numberOfDescendants(SinglePointCrossover(0.1)) == 2
+  @test SinglePointCrossover(probability = 0.1).probability == 0.1
+  @test numberOfRequiredParents(SinglePointCrossover(probability = 0.1)) == 2
+  @test numberOfDescendants(SinglePointCrossover(probability = 0.1)) == 2
 
   @test singlePointCrossoverWithProbabilityZeroReturnTwoSolutionsEqualToTheParentSolutions()
   @test singlePointCrossoverWithWithProbabilityOneChangesAllTheVariableValuesIntheReturnedSolutions()
@@ -61,7 +61,7 @@ function SBXCrossoverWithAEmptyListOfBoundsRaiseAnException()
   return SBXCrossover(probability = 1.0, distributionIndex = 20.0, bounds = [])
 end
 
-sbxCrossover = SBXCrossover(probability = 0.054, probability = 20.0, probability = [Bounds{Float64}(3.0, 5.0)])
+sbxCrossover = SBXCrossover(probability = 0.054, distributionIndex = 20.0, bounds = [Bounds{Float64}(3.0, 5.0)])
 @testset "SBX crossover tests" begin
   @test sbxCrossover.probability == 0.054
   @test sbxCrossover.distributionIndex == 20.0