public void GivenRandom_WhenGetIntWithTabu_ThenShouldReturnDifferent()
{
var random = new PredeterminedRandom(10, 10, 25, 30);
var result = random.GetIntWithTabu(0, 50, 10);
result.Should().Be(25);
}
public void GivenSortedSubsetChromosomes_WhenOnePointCrossOver_WithConflict_ThenShouldReply()
{
var chromosomes = SortedSubsetTestData.CreateChromosomes();
var random = new PredeterminedRandom(5, 7); //Crossover gene value before and after conflicts
var parameterSet = new ParameterSet();
parameterSet.SetValue(ParameterNames.FailedCrossoverRetryCount, 1);
var conflictDetectors = new List <INeighborhoodConflictDetector>()
{
new PredeterminedConflictDetector(false, true, true, false, false, false, false, false, false, false)
};
var crossover = new OnePointCrossover(random, parameterSet, conflictDetectors);
var results = crossover.Cross(chromosomes[0], chromosomes[1]);
var result1 = results[0] as SortedSubsetChromosome;
var result2 = results[1] as SortedSubsetChromosome;
result1.Sections[0].Should().BeEquivalentTo(new int[] { 1, 4, 5, 9 });
result1.Sections[1].Should().BeEquivalentTo(new int[] { 3, 6, 7 });
result1.Sections[2].Should().BeEquivalentTo(new int[] { 2, 8 });
result2.Sections[0].Should().BeEquivalentTo(new int[] { 3, 4, 7 });
result2.Sections[1].Should().BeEquivalentTo(new int[] { 2, 5, 8 });
result2.Sections[2].Should().BeEquivalentTo(new int[] { 1, 6, 9 });
}
public void GivenSortedSubsetChromosomes_WhenOnePointCrossOver_ThenShouldBeProper()
{
var chromosomes = SortedSubsetTestData.CreateChromosomes();
var random = new PredeterminedRandom(5); //Crossover gene value
var parameterSet = new ParameterSet();
parameterSet.SetValue(ParameterNames.FailedCrossoverRetryCount, 0);
var conflictDetectors = new List <INeighborhoodConflictDetector>()
{
AllRightConflictDetector.Instance
};
var crossover = new OnePointCrossover(random, parameterSet, conflictDetectors);
var results = crossover.Cross(chromosomes[0], chromosomes[1]);
var result1 = results[0] as SortedSubsetChromosome;
var result2 = results[1] as SortedSubsetChromosome;
result1.Sections[0].Should().BeEquivalentTo(new int[] { 1, 4, 9 });
result1.Sections[1].Should().BeEquivalentTo(new int[] { 3, 5, 7 });
result1.Sections[2].Should().BeEquivalentTo(new int[] { 2, 6, 8 });
result2.Sections[0].Should().BeEquivalentTo(new int[] { 3, 4, 5, 7 });
result2.Sections[1].Should().BeEquivalentTo(new int[] { 2, 6, 8 });
result2.Sections[2].Should().BeEquivalentTo(new int[] { 1, 9 });
}
public void NonEmptyProbabilityDistribution_ChooseOne_ShouldReturnOne()
{
var random = new PredeterminedRandom(80, 30);
var distribution = new StochasticProvider <string>(random)
.Add("First", 50)
.Add("Second", 40);
distribution.GetOne().Should().Be("Second");
distribution.GetOne().Should().Be("First");
}
public void SwapTwoRangeMutation_Mutate_ReturnMutated()
{
var random = new PredeterminedRandom(new double[] { 5, 3, 2, 2 });
var parameterSet = new ParameterSet();
var chromosome = PermutationTestData.CreateTestChromosome1(); //{ 7, 2, 8, 4, 1, 6, 3, 5, 9, 0 }
var mutation = new SwapTwoRangeMutation(random, parameterSet, null);
var result = mutation.Mutate(chromosome);
var expected = new int[] { 7, 2, 6, 3, 5, 1, 8, 4, 9, 0 };
result.Genes.Should().BeEquivalentTo(expected);
}
public void PMXCrossover_Cross_ReturnChildren(int position, int length, int[] expected)
{
var random = new PredeterminedRandom(new double[] { position, length });
var parameterSet = new ParameterSet();
var crossover = new PMXCrossover(random, parameterSet, null);
var parents = PermutationTestData.CreateTestChromosomes();
var children = crossover.Cross(parents[0], parents[1]);
children.Count.Should().Be(2);
((PermutationChromosome)children[0]).Genes.Should().BeEquivalentTo(expected);
}
public void PMXCrossover_CreateGeneMap_GetUniqueValue_ReturnUnique(int position, int expected)
{
var random = new PredeterminedRandom(new double[0]);
var parameterSet = new ParameterSet();
var crossover = new PMXCrossover(random, parameterSet, null);
var chromosomes = PermutationTestData.CreateTestChromosomes();
var range = new GeneRange(3, 4);
var geneMap = crossover.GenerateGeneMap(((PermutationChromosome)chromosomes[0]).Genes, range);
var result = crossover.GetUniqueGeneValue(((PermutationChromosome)chromosomes[1]).Genes, geneMap, position);
result.Should().Be(expected);
}
public void DoubleFieldOperator_Set_ShouldSet(double change, double expected)
{
var testStruct = new TestStruct()
{
DoubleField = 4.0,
BitField = false,
IntegerField = 0
};
var random = new PredeterminedRandom(new double[] { 1, 1 });
var parameterSet = new ParameterSet(new List <PeaSettingsNamedValue>()
{
new PeaSettingsNamedValue(ParameterNames.MutationIntensity, change)
});
var fieldOperator = new DoubleFieldOperator <TestStruct>("DoubleField", random, parameterSet);
fieldOperator.Mutate(testStruct);
}
public void GivenSortedSubsetChromosomes_WhenOnePointCrossOver_WithConflict_ThenShouldReturnsZero()
{
var chromosomes = SortedSubsetTestData.CreateChromosomes();
var random = new PredeterminedRandom(5); //Crossover gene value
var parameterSet = new ParameterSet();
parameterSet.SetValue(ParameterNames.FailedCrossoverRetryCount, 0);
var conflictDetectors = new List <INeighborhoodConflictDetector>()
{
new PredeterminedConflictDetector(false, true, true)
};
var crossover = new OnePointCrossover(random, parameterSet, conflictDetectors);
var results = crossover.Cross(chromosomes[0], chromosomes[1]);
results.Count.Should().Be(0);
}
public void GivenSortedSubsetOnePointCrossover_WhenMergeSections_ThenShouldBeProper(
int[] sectionForLeft, int leftEndPosition,
int[] sectionForRight, int rightStartPosition, int[] expected)
{
bool childAlreadyConflicted = false;
var random = new PredeterminedRandom(5);
var parameterSet = new ParameterSet();
var conflictDetectors = new List <INeighborhoodConflictDetector>()
{
AllRightConflictDetector.Instance
};
var crossover = new OnePointCrossover(random, parameterSet, conflictDetectors);
var result = crossover.MergeSections(sectionForLeft, leftEndPosition, sectionForRight, rightStartPosition,
ref childAlreadyConflicted);
result.Should().BeEquivalentTo(expected);
}
public void PMXCrossover_CopyWithDuplicationElimination_CopyData(int begin, int end, int[] expected)
{
var random = new PredeterminedRandom(new double[0]);
var parameterSet = new ParameterSet();
var crossover = new PMXCrossover(random, parameterSet, null);
var chromosomes = PermutationTestData.CreateTestChromosomes();
var range = new GeneRange(3, 3);
var geneMap = crossover.GenerateGeneMap(((PermutationChromosome)chromosomes[0]).Genes, range);
var childGenes = new int[((PermutationChromosome)chromosomes[0]).Genes.Length];
var length = end - begin;
crossover.CopyWithDuplicationElimination(((PermutationChromosome)chromosomes[1]).Genes, childGenes, geneMap, begin, end);
var result = new int[length];
Array.Copy(childGenes, begin, result, 0, length);
result.Should().BeEquivalentTo(expected);
}
public void GivenChromosome_WithoutConflict_ThenShouldBeReplaceRandom()
{
var random = new PredeterminedRandom(1, 2, 1, 2); //possibility, sourceSection, sourcePosition, wrongTargetSection, goodTargetSection
var parameterSet = new ParameterSet();
parameterSet.SetValue(ParameterNames.FailedMutationRetryCount, 0);
var chromosome = SortedSubsetTestData.CreateChromosome();
var conflictDetectors = new List <INeighborhoodConflictDetector>()
{
AllRightConflictDetector.Instance
};
var mutation = new ReplaceOneGeneMutation(random, parameterSet, conflictDetectors);
var result = mutation.Mutate(chromosome);
result.Sections[0].Should().BeEquivalentTo(new int[] { 1, 4, 5, 7 });
result.Sections[1].Should().BeEquivalentTo(new int[] { 3, 6 });
result.Sections[2].Should().BeEquivalentTo(new int[] { 2, 8, 9 });
}
public void Order1Crossover_Cross_ReturnChildren()
{
var position = 3;
var length = 5;
var random = new PredeterminedRandom(new double[] { position, length });
var parameterSet = new ParameterSet();
var crossover = new Order1Crossover(random, parameterSet, null);
var parent1Genes = new int[] { 8, 4, 7, 3, 6, 2, 5, 1, 9, 0 };
var parent2Genes = new int[] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 };
var parent1 = new PermutationChromosome(parent1Genes);
var parent2 = new PermutationChromosome(parent2Genes);
var children = crossover.Cross(parent1, parent2);
children.Count.Should().Be(2);
var expected = new int[] { 0, 4, 7, 3, 6, 2, 5, 1, 8, 9 };
((PermutationChromosome)children[0]).Genes.Should().BeEquivalentTo(expected);
}
public void GivenChromosome_WithConflict_WhenReplaceOneGene_ThenShouldBeReplaceConflicted()
{
var random = new PredeterminedRandom(0.5, 3, 1); //possibility, conflictIndex, targetSection
var parameterSet = new ParameterSet();
parameterSet.SetValue(ParameterNames.ConflictReducingProbability, 0.6);
parameterSet.SetValue(ParameterNames.FailedMutationRetryCount, 0);
var chromosome = SortedSubsetTestData.CreateChromosome();
chromosome.ConflictList.Add(new GenePosition(0, 1));
var conflictDetectors = new List <INeighborhoodConflictDetector>()
{
AllRightConflictDetector.Instance
};
var mutation = new ReplaceOneGeneMutation(random, parameterSet, conflictDetectors);
var result = mutation.Mutate(chromosome);
result.Sections[0].Should().BeEquivalentTo(new int[] { 1, 5, 7 });
result.Sections[1].Should().BeEquivalentTo(new int[] { 3, 4, 6, 8 });
result.Sections[2].Should().BeEquivalentTo(new int[] { 2, 9 });
}
public void GivenPredeterminedRandom_WhenGetDoubleWithMinMax_ThenShouldReturnProper(double minValue, double maxValue)
{
var random = new PredeterminedRandom(100.17, 101.33, 3.141592654, 2.71828);
TestRandomGetDouble(random, minValue, maxValue);
}
public void GivenPredeterminedRandom_WhenGetIntWithMinMax_ThenShouldReturnProper(int minValue, int maxValue)
{
var random = new PredeterminedRandom(100, 101, 102, 103);
TestRandomGetInt(random, minValue, maxValue);
}
