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 ShouldComposeNewProcessInformationForNewGeneration()
{
Random rng = new Random();
for (Int32 i = 0; i < TestConstants.TestRepeats; i++)
{
Int32 populationSize = rng.Next(1, 1000);
Int32 geneSize = rng.Next(1, 1000);
Population <TestIndividual <Int32>, Int32> GetPopulation()
{
var population = new Population <TestIndividual <Int32>, Int32>();
IList <TestIndividual <Int32> > individuals = new List <TestIndividual <Int32> >();
for (Int32 j = 0; j < populationSize; j++)
{
var newIndividual = new TestIndividual <Int32>();
Chromosome <Int32> newChromosome =
ChromosomeInitializers.GetRandomChromosome <Chromosome <Int32>, Int32>(geneSize
, rng_ => ChromosomeInitializers.GetRandomInt32(rng_, 0, 10));
newIndividual.SetChromosome(newChromosome);
individuals.Add(newIndividual);
}
population.SetPopulation(individuals);
population.OrderByFitness(new PopulationTests.TestFitnessFunctionInt32());
return(population);
}
Population <TestIndividual <Int32>, Int32> population1 = GetPopulation();
Population <TestIndividual <Int32>, Int32> population2 = GetPopulation();
var composer =
new ProcessInformationComposer <Population <TestIndividual <Int32>, Int32>, TestIndividual <Int32>,
Int32>();
ProcessInformation firstProcessInformation = composer.ComposeProcessInformation(null, population1);
ProcessInformation secondProcessInformation =
composer.ComposeProcessInformation(firstProcessInformation, population2);
Assert.Equal(firstProcessInformation.Generation, secondProcessInformation.Generation - 1);
Assert.Equal(firstProcessInformation.TotalFitnessDelta, firstProcessInformation.TotalFitness);
Assert.Equal(firstProcessInformation.FittestFitnessDelta, firstProcessInformation.FittestFitness);
Assert.Equal(firstProcessInformation.FittestNFitnessDelta, firstProcessInformation.FittestNFitness);
Assert.Equal(secondProcessInformation.TotalFitnessDelta
, secondProcessInformation.TotalFitness - firstProcessInformation.TotalFitness);
Assert.Equal(secondProcessInformation.FittestFitnessDelta
, secondProcessInformation.FittestFitness - firstProcessInformation.FittestFitness);
Assert.Equal(secondProcessInformation.FittestNFitnessDelta
, secondProcessInformation.FittestNFitness - firstProcessInformation.FittestNFitness);
}
}
public void RandomChromosomeInitializerShouldWork()
{
Random rng = new Random();
Int32 chromosomeLength = rng.Next(1, 1000_000);
Int32 geneMin = rng.Next(1, 1000_000);
Int32 geneMax = rng.Next(geneMin, 1000_000);
// Int32
Func <Random, Int32> randomProvider = ChromosomeInitializers.CreateRandomProvider(geneMin
, geneMax, ChromosomeInitializers.GetRandomInt32);
Chromosome <Int32> chromosome =
ChromosomeInitializers.GetRandomChromosome <Chromosome <Int32>, Int32>(chromosomeLength, randomProvider);
Int32[] fullRange = chromosome.GetGeneRange(0, chromosomeLength);
Assert.Equal(chromosomeLength, fullRange.Length);
foreach (Int32 gene in fullRange)
{
Assert.True(gene >= geneMin);
Assert.True(gene <= geneMax);
}
// Boolean
Func <Random, Boolean> randomProvider2 = ChromosomeInitializers.CreateRandomProvider(false
, true, ChromosomeInitializers.GetRandomBoolean);
Chromosome <Boolean> chromosome2 =
ChromosomeInitializers.GetRandomChromosome <Chromosome <Boolean>, Boolean>(chromosomeLength
, randomProvider2);
Boolean[] fullRange2 = chromosome2.GetGeneRange(0, chromosomeLength);
Assert.Equal(chromosomeLength, fullRange2.Length);
// Double
Func <Random, Double> randomProvider3 = ChromosomeInitializers.CreateRandomProvider(Convert.ToDouble(geneMin)
, Convert.ToDouble(geneMax), ChromosomeInitializers.GetRandomDouble);
Chromosome <Double> chromosome3 =
ChromosomeInitializers.GetRandomChromosome <Chromosome <Double>, Double>(chromosomeLength
, randomProvider3);
Double[] fullRange3 = chromosome3.GetGeneRange(0, chromosomeLength);
Assert.Equal(chromosomeLength, fullRange3.Length);
}
public static TestIndividual <Int32>[] GetRandomTestIndividuals(Int32 parentCount, Int32 chromosomeSize
, Int32 geneMin = 0, Int32 geneMax = 10)
{
var individuals = new TestIndividual <Int32> [parentCount];
Random rng = new Random();
for (Int32 i = 0; i < parentCount; i++)
{
individuals[i] = GetRandomTestIndividual(chromosomeSize
, () => ChromosomeInitializers.GetRandomInt32(rng, geneMin, geneMax));
}
return(individuals);
}
public void ShouldFetchFittestIndividualsAsParents()
{
Random rng = new Random();
for (Int32 i = 0; i < TestConstants.TestRepeats; i++)
{
Int32 populationSize = rng.Next(2, 1000);
Population <TestIndividual <Int32>, Int32> population = TestUtils.GetPopulation(populationSize
, () => TestUtils.GetRandomTestIndividual(10
, () => ChromosomeInitializers.GetRandomInt32(rng, 0, 10)));
population.OrderByFitness(new PopulationTests.TestFitnessFunctionInt32());
var selector =
new SingleGroupFittestParentsSelector <Population <TestIndividual <Int32>, Int32>,
TestIndividual <Int32>, Int32>(2);
IList <IEnumerable <TestIndividual <Int32> > > selectedParents = selector.GetParents(population).ToList();
IEnumerable <TestIndividual <Int32> > selectedParentPair = selectedParents.First();
Assert.Equal(1, selectedParents.Count);
IList <IndividualWithFitness <TestIndividual <Int32>, Int32> > twoFittest = population.GetNFittest(2);
foreach (IndividualWithFitness <TestIndividual <Int32>, Int32> fitIndividual in twoFittest)
{
Assert.Contains(fitIndividual.GetIndividual(), selectedParentPair);
}
}
}
public void ShouldTerminateBasedOnGeneration()
{
Random rng = new Random();
Population <TestIndividual <Int32>, Int32> population = TestUtils.GetPopulation(10
, () => TestUtils.GetRandomTestIndividual(10, () => ChromosomeInitializers.GetRandomInt32(rng, -5, 5)));
population.OrderByFitness(new PopulationTests.TestFitnessFunctionInt32());
for (Int32 i = 0; i < TestConstants.TestRepeats; i++)
{
Int32 maxRun = 1000;
Int32 terminationPoint = rng.Next(1, maxRun);
var composer =
new ProcessInformationComposer <Population <TestIndividual <Int32>, Int32>, TestIndividual <Int32>,
Int32>();
var terminator =
new GenerationsCountTerminator <Population <TestIndividual <Int32>, Int32>, TestIndividual <Int32>,
Int32>((UInt64)terminationPoint);
ProcessInformation previousProcessInformation = null;
for (Int32 j = 1; j < maxRun; j++)
{
ProcessInformation newProcessInformation =
composer.ComposeProcessInformation(previousProcessInformation, population);
previousProcessInformation = newProcessInformation;
Boolean shouldTerminate = terminator.ShouldTerminate(population, newProcessInformation);
if (j >= terminationPoint)
{
Assert.True(shouldTerminate);
}
else
{
Assert.False(shouldTerminate);
}
}
}
}
public void ShouldOrderByFitness()
{
Random rng = new Random();
for (Int32 i = 0; i < TestConstants.TestRepeats; i++)
{
Int32 populationSize = rng.Next(1, 1000);
Int32 chromosomeSize = rng.Next(1, 1000);
Population <TestIndividual <Double>, Double> population = TestUtils.GetPopulation(populationSize
, () => TestUtils.GetRandomTestIndividual(chromosomeSize
, () => ChromosomeInitializers.GetRandomDouble(rng, 0, 100)));
List <(TestIndividual <Double> individual, Double)> individuals = population.GetIndividuals()
.Select(individual => (individual, individual.GetChromosome().GetGenome().Sum())).ToList();
individuals = individuals.OrderBy(tuple => tuple.Item2).ToList();
(TestIndividual <Double> individual, Double)[] expectedOrdering = individuals.ToArray();
public void ShouldNotAllowAccessToDirtyOrdering()
{
Random rng = new Random();
Population <TestIndividual <Int32>, Int32> population = TestUtils.GetPopulation(100
, () => TestUtils.GetRandomTestIndividual(100
, () => ChromosomeInitializers.GetRandomInt32(rng, 0, 10)));
Assert.Throws <InvalidOperationException>(() => population.GetFittest());
Assert.Throws <InvalidOperationException>(() => population.GetNFittest(1));
Assert.Throws <InvalidOperationException>(() => population.GetPopulationByFitness());
}
public void ShouldComposeNewFirstProcessInformation()
{
Random rng = new Random();
Population <TestIndividual <Int32>, Int32> population = TestUtils.GetPopulation(10
, () => TestUtils.GetRandomTestIndividual(10, () => ChromosomeInitializers.GetRandomInt32(rng, 0, 10)));
population.OrderByFitness(new PopulationTests.TestFitnessFunctionInt32());
var composer =
new ProcessInformationComposer <Population <TestIndividual <Int32>, Int32>, TestIndividual <Int32>, Int32
>();
ProcessInformation newProcessInformation = composer.ComposeProcessInformation(null, population);
Assert.Equal((UInt64)1, newProcessInformation.Generation);
Double expectedTotalFitness = population
.GetPopulationByFitness().Select(fitness => fitness.GetFitness()).Sum();
Assert.Equal(expectedTotalFitness, newProcessInformation.TotalFitness);
}
