public void CrossoverRandomlyDecidesOnParentToTakeGeneValueFromForSingleGene()
{
// Build genome builder with a parameter tree that consists of a single continuous parameter.
string parameterName = "parameter";
IParameterNode parameter = new ValueNode <double>(parameterName, new ContinuousDomain());
var genomeBuilder = new GenomeBuilder(
new ParameterTree(parameter),
new AlgorithmTunerConfiguration.AlgorithmTunerConfigurationBuilder().Build(maximumNumberParallelEvaluations: 1));
// Build genomes with different parameter values.
var parent1 = new Genome();
var parent2 = new Genome();
Allele <double> parent1Allele = new Allele <double>(0);
Allele <double> parent2Allele = new Allele <double>(1);
parent1.SetGene(parameterName, parent1Allele);
parent2.SetGene(parameterName, parent2Allele);
// Observe what gene value the children's genes have.
int numberLoops = 1000;
int observedInheritanceFromParent1 = 0;
for (int i = 0; i < numberLoops; i++)
{
var child = genomeBuilder.Crossover(parent1, parent2);
if (object.Equals(parent1Allele, child.GetGeneValue(parameterName)))
{
observedInheritanceFromParent1++;
}
}
double[] observed = { observedInheritanceFromParent1, numberLoops - observedInheritanceFromParent1 };
// We would expect each value the same number of times:
double[] expected = { numberLoops / 2, numberLoops / 2 };
// Use Chi-Squared Test.
var equalProbabilityTest = new ChiSquareTest(expected, observed, degreesOfFreedom: numberLoops - 1);
Assert.False(
equalProbabilityTest.Significant,
$"Single gene was found to be not equally distributed in crossovers by the Chi-Squared test with significance level of {equalProbabilityTest.Size}.");
}
public void CrossoverRespectsSwitchProbability()
{
// Build parameter tree that consists of two dependent continuous parameters.
string rootParameterName = "parameterRoot";
string childParameterName = "parameterChild";
var rootParameter = new ValueNode <double>(rootParameterName, new ContinuousDomain());
var childParameter = new ValueNode <double>(childParameterName, new ContinuousDomain());
rootParameter.SetChild(childParameter);
// Build genome builder with that parameter tree and a specific crossover switch probability.
double crossoverSwitchParameter = 0.25;
AlgorithmTunerConfiguration config = new AlgorithmTunerConfiguration.AlgorithmTunerConfigurationBuilder()
.SetCrossoverSwitchProbability(crossoverSwitchParameter)
.Build(maximumNumberParallelEvaluations: 1);
var genomeBuilder = new GenomeBuilder(new ParameterTree(rootParameter), config);
// Build parents.
var parent1 = new Genome();
var parent2 = new Genome();
var parent1RootAllele = new Allele <double>(1);
var parent1ChildAllele = new Allele <double>(2);
var parent2RootAllele = new Allele <double>(3);
var parent2ChildAllele = new Allele <double>(4);
parent1.SetGene(rootParameterName, parent1RootAllele);
parent1.SetGene(childParameterName, parent1ChildAllele);
parent2.SetGene(rootParameterName, parent2RootAllele);
parent2.SetGene(childParameterName, parent2ChildAllele);
// Observe if children's genes come from the same parent or not.
int numberLoops = 1000;
int genesCameFromSameParent = 0;
for (int i = 0; i < numberLoops; i++)
{
var child = genomeBuilder.Crossover(parent1, parent2);
IAllele rootAllele = child.GetGeneValue(rootParameterName);
IAllele childAllele = child.GetGeneValue(childParameterName);
bool geneValuesInheritedFromSameParent =
(object.Equals(rootAllele, parent1RootAllele) && object.Equals(childAllele, parent1ChildAllele)) ||
(object.Equals(rootAllele, parent2RootAllele) && object.Equals(childAllele, parent2ChildAllele));
if (geneValuesInheritedFromSameParent)
{
genesCameFromSameParent++;
}
}
double[] observed = { genesCameFromSameParent, numberLoops - genesCameFromSameParent };
// We would expect each case according to switch probability:
int expectedSwitches = (int)(crossoverSwitchParameter * numberLoops);
double[] expected = { numberLoops - expectedSwitches, expectedSwitches };
// Use Chi-Squared Test.
var matchesSwitchProbabilityTest = new ChiSquareTest(expected, observed, degreesOfFreedom: numberLoops - 1);
Assert.False(
matchesSwitchProbabilityTest.Significant,
$"Crossover was found not to respect the switch probability by the Chi-Squared test with significance level of {matchesSwitchProbabilityTest.Size}.");
}