public void Cross_DocumentationSample_Child()
{
var target = new VotingRecombinationCrossover(4, 3);
// 1 4 3 5 2 6
var chromosome1 = Substitute.For <ChromosomeBase>(6);
chromosome1.ReplaceGenes(0, new Gene[] {
new Gene(1),
new Gene(4),
new Gene(3),
new Gene(5),
new Gene(2),
new Gene(6)
});
var child = Substitute.For <ChromosomeBase>(6);
child.GenerateGene(2).Returns(new Gene(22));
child.GenerateGene(3).Returns(new Gene(33));
child.GenerateGene(4).Returns(new Gene(44));
chromosome1.CreateNew().Returns(child);
// 1 2 4 3 5 6
var chromosome2 = Substitute.For <ChromosomeBase>(6);
chromosome2.ReplaceGenes(0, new Gene[]
{
new Gene(1),
new Gene(2),
new Gene(4),
new Gene(3),
new Gene(5),
new Gene(6)
});
// 3 2 1 5 4 6
var chromosome3 = Substitute.For <ChromosomeBase>(6);
chromosome3.ReplaceGenes(0, new Gene[]
{
new Gene(3),
new Gene(2),
new Gene(1),
new Gene(5),
new Gene(4),
new Gene(6)
});
// 1 2 3 4 5 6
var chromosome4 = Substitute.For <ChromosomeBase>(6);
chromosome4.ReplaceGenes(0, new Gene[]
{
new Gene(1),
new Gene(2),
new Gene(3),
new Gene(4),
new Gene(5),
new Gene(6)
});
var actual = target.Cross(new List <IChromosome>()
{
chromosome1, chromosome2, chromosome3, chromosome4
});
Assert.AreEqual(1, actual.Count);
var actualChild = actual[0];
Assert.AreEqual(6, actualChild.Length);
Assert.AreEqual(1, actualChild.GetGene(0).Value);
Assert.AreEqual(2, actualChild.GetGene(1).Value);
Assert.AreEqual(22, actualChild.GetGene(2).Value);
Assert.AreEqual(33, actualChild.GetGene(3).Value);
Assert.AreEqual(44, actualChild.GetGene(4).Value);
Assert.AreEqual(6, actualChild.GetGene(5).Value);
}
private void EvolveGeneticStrategyButton_Click(object sender, RoutedEventArgs e)
{
OutputTextBlock.Text = "Evolving...";
Task.Run(() =>
{
var chromosome = new BlackjackChromosome();
var fitness = new BlackjackFitness();
var population = new Population(Settings.GeneticSettings.MinPopulationSize, Settings.GeneticSettings.MaxPopulationSize, chromosome);
ISelection selection;
switch (Settings.GeneticSettings.SelectionType)
{
case SelectionType.Elite:
selection = new EliteSelection();
break;
case SelectionType.RouletteWheel:
selection = new RouletteWheelSelection();
break;
case SelectionType.StochasticUniversalSampling:
selection = new StochasticUniversalSamplingSelection();
break;
case SelectionType.Tournament:
selection = new TournamentSelection(Settings.GeneticSettings.TournamentSize);
break;
default:
throw new InvalidOperationException();
}
ICrossover crossover;
switch (Settings.GeneticSettings.CrossoverType)
{
case CrossoverType.AlternatingPosition:
crossover = new AlternatingPositionCrossover();
break;
case CrossoverType.CutAndSplice:
crossover = new CutAndSpliceCrossover();
break;
case CrossoverType.Cycle:
crossover = new CycleCrossover();
break;
case CrossoverType.OnePoint:
crossover = new OnePointCrossover();
break;
case CrossoverType.TwoPoint:
crossover = new TwoPointCrossover();
break;
case CrossoverType.OrderBased:
crossover = new OrderBasedCrossover();
break;
case CrossoverType.Ordered:
crossover = new OrderedCrossover();
break;
case CrossoverType.PartiallyMapped:
crossover = new PartiallyMappedCrossover();
break;
case CrossoverType.PositionBased:
crossover = new PositionBasedCrossover();
break;
case CrossoverType.ThreeParent:
crossover = new ThreeParentCrossover();
break;
case CrossoverType.Uniform:
crossover = new UniformCrossover(Settings.Current.GeneticSettings.MixProbability);
break;
case CrossoverType.VotingRecombination:
crossover = new VotingRecombinationCrossover();
break;
default:
throw new InvalidOperationException();
}
var mutation = new UniformMutation();
var termination = new FitnessStagnationTermination(Settings.Current.GeneticSettings.NumStagnantGenerations);
var taskExecutor = new ParallelTaskExecutor();
var ga = new GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation);
ga.Termination = termination;
ga.TaskExecutor = taskExecutor;
ga.MutationProbability = Settings.GeneticSettings.MutationProbability;
ga.CrossoverProbability = Settings.GeneticSettings.CrossoverProbability;
var latestFitness = double.MinValue;
ga.GenerationRan += (s, o) =>
{
geneticStrategy = (IStrategy)ga.BestChromosome;
var generationNumber = ga.GenerationsNumber;
var bestFitness = ga.BestChromosome.Fitness.Value;
var avgFitness = ga.Population.CurrentGeneration.Chromosomes.Average(c => c.Fitness.Value);
Dispatcher.Invoke(() =>
{
if (generationNumber == 1)
{
OutputTextBlock.Text = string.Empty;
}
OutputTextBlock.Text = $"Gen: {generationNumber}\tFit: {bestFitness}\tAvg: {avgFitness.ToString("0")}\n" + OutputTextBlock.Text;
if (bestFitness != latestFitness)
{
latestFitness = bestFitness;
var savedImageName = Settings.Current.GeneticSettings.SaveImagePerGeneration ? "gen" + generationNumber : null;
StrategyViewer.Draw(GeneticStrategyCanvas, geneticStrategy, $"Best from generation {generationNumber}", savedImageName);
}
}, DispatcherPriority.Background);
};
ga.TerminationReached += (s, o) =>
{
Dispatcher.Invoke(() =>
{
OutputTextBlock.Text = "Termination reached.\n" + OutputTextBlock.Text;
TestGeneticStrategyButton.IsEnabled = true;
}, DispatcherPriority.Background);
};
ga.Start();
});
}
public IList <IChromosome> VotingRecombinationCrossover()
{
var target = new VotingRecombinationCrossover();
return(target.Cross(CreateThreeParents()));
}