public void MutatorModifierShouldMutateGenome()
{
Random rng = new Random();
Int32 RandomGeneProvider()
{
return(ChromosomeInitializers.GetRandomInt32(rng, 0, 10));
}
for (Int32 r = 0; r < TestConstants.TestRepeats; r++)
{
Double mutationRatio = rng.NextDouble();
var mutator =
new RandomMutationModifier <Population <TestIndividual <Int32>, Int32>, TestIndividual <Int32>, Int32>(
mutationRatio, RandomGeneProvider);
Int32 chromosomeSize = 10000;
Int32 populationCount = 100;
Int32 geneMin = 0;
Int32 geneMax = 100;
Population <TestIndividual <Int32>, Int32> population = TestUtils.GetPopulation(populationCount
, () => TestUtils.GetRandomTestIndividual(chromosomeSize
, () => ChromosomeInitializers.GetRandomInt32(rng, geneMin, geneMax)));
Int32[][] originalGenes = population.GetIndividuals()
.Select(individual => individual.GetChromosome().GetGenome()).ToArray();
originalGenes = Utils.DeepCopy(originalGenes);
Population <TestIndividual <Int32>, Int32> modifiedPopulation = mutator.ModifyPopulation(population);
Int32 modified = 0;
Int32 unmodified = 0;
TestIndividual <Int32>[] modifiedIndividuals = modifiedPopulation.GetIndividuals().ToArray();
for (Int32 i = 0; i < modifiedIndividuals.Length; i++)
{
Int32[] modifiedGenes = modifiedIndividuals[i].GetChromosome().GetGenome();
for (Int32 j = 0; j < originalGenes.Length; j++)
{
if (originalGenes[i][j] == modifiedGenes[j])
{
unmodified++;
}
else
{
modified++;
}
}
}
// Verify that the ratio of mutated genes is about the same as specified to modifier.
Double mutatedRatio = (Double)modified / (modified + unmodified);
mutatedRatio.AreDoublesEqual(mutationRatio, 0.02);
}
}
public void TestFitnessFunctionShouldWork()
{
Random rng = new Random();
Int32 genomeLength = rng.Next(1, 1000);
// Test fitness on boolean genome.
for (Int32 i = 0; i < 5; i++)
{
Double expectedFitness = 0;
var individual = new TestIndividual <Boolean>();
var chromosome = new Chromosome <Boolean>();
chromosome.InitializeChromosome(genomeLength);
IList <Boolean> testChromosome = new List <Boolean>();
for (Int32 j = 0; j < genomeLength; j++)
{
Boolean gene = rng.Next(2) == 1;
if (gene)
{
expectedFitness++;
}
testChromosome.Add(gene);
}
chromosome.SetChromosome(testChromosome.ToArray());
individual.SetChromosome(chromosome);
TestFitnessFunctionBoolean fitnessFunction = new TestFitnessFunctionBoolean();
Double fitness = fitnessFunction.CalculateFitness(individual);
Assert.True(fitness.AreDoublesEqual(expectedFitness));
}
// Test fitness on double genome.
for (Int32 i = 0; i < 5; i++)
{
Double expectedFitness = 0;
var individual = new TestIndividual <Double>();
var chromosome = new Chromosome <Double>();
chromosome.InitializeChromosome(genomeLength);
IList <Double> testChromosome = new List <Double>();
for (Int32 j = 0; j < genomeLength; j++)
{
Double gene = rng.NextDouble() * 5;
expectedFitness += gene;
testChromosome.Add(gene);
}
chromosome.SetChromosome(testChromosome.ToArray());
individual.SetChromosome(chromosome);
TestFitnessFunctionDouble fitnessFunction = new TestFitnessFunctionDouble();
Double fitness = fitnessFunction.CalculateFitness(individual);
Assert.True(fitness.AreDoublesEqual(expectedFitness));
}
}
public void TrivialMaximizeTruesProcessShouldRun()
{
Random rng = new Random();
Int32 genomeSize = 100;
Int32 targetFitness = 90;
Int32 populationSize = 100;
Double mutationRate = 0.01;
var matingFunctions = new List <IMatingFunction <TestIndividual <Boolean>, Chromosome <Boolean>, Boolean> >
{
new CrossOverMatingFunction <TestIndividual <Boolean>, Chromosome <Boolean>, Boolean>(1)
, new CrossOverMatingFunction <TestIndividual <Boolean>, Chromosome <Boolean>, Boolean>(3)
, new RandomCombinationMatingFunction <TestIndividual <Boolean>, Chromosome <Boolean>, Boolean>()
};
foreach (IMatingFunction <TestIndividual <Boolean>, Chromosome <Boolean>, Boolean> matingFunction in
matingFunctions)
{
var builder =
new GeneticSelectionProcess <Population <TestIndividual <Boolean>, Boolean>, TestIndividual <Boolean>,
Chromosome <Boolean>, Boolean, ProcessInformation> .Builder();
var process = builder.SetInitialPopulationSize(populationSize).UseDefaultConsoleLogger()
.SetProcessInformationComposer(
new ProcessInformationComposer <Population <TestIndividual <Boolean>, Boolean>,
TestIndividual <Boolean>, Boolean>()).SetMatingFunction(matingFunction)
.SetParentSelector(
new SingleGroupFittestParentsSelector <Population <TestIndividual <Boolean>, Boolean>,
TestIndividual <Boolean>, Boolean>()).SetFitnessFunction(new TestFitnessFunction())
.SetSurvivorSelector(
new ReplaceLeastFitSelector <Population <TestIndividual <Boolean>, Boolean>,
TestIndividual <Boolean>, Boolean>())
.SetPopulationInitializer(
new BooleanRandomPopulationInitializer <Population <TestIndividual <Boolean>, Boolean>,
TestIndividual <Boolean>, Chromosome <Boolean> >(genomeSize))
.AddTerminationCondition(
new FitnessTerminator <Population <TestIndividual <Boolean>, Boolean>, TestIndividual <Boolean>,
Boolean>(targetFitness))
.AddPopulationModifier(
new RandomMutationModifier <Population <TestIndividual <Boolean>, Boolean>, TestIndividual <Boolean>
, Boolean>(mutationRate, () => rng.Next(2) == 1)).Build();
process.Initialize();
process.RunUntilConvergence();
IndividualWithFitness <TestIndividual <Boolean>, Boolean> fittest = process.GetPopulation().GetFittest();
Double fittestFitness = fittest.GetFitness();
Boolean[] fittestGenome = fittest.GetIndividual().GetChromosome().GetGenome();
Double expectedFitness = fittestGenome.Select(b => b ? 1 : 0).Sum();
Assert.True(fittestFitness >= 90);
Assert.True(expectedFitness >= 90);
Assert.True(expectedFitness.AreDoublesEqual(fittestFitness));
IList <ProcessInformation> history = process.GetHistory();
Assert.True(history.Count > 1);
Assert.True(fittestFitness.AreDoublesEqual(history[history.Count - 1].FittestFitness));
}
}
