public void GA_Issue61_Solved()
{
const Int32 FinalAns = 4567213;
var chromosome = new Issue61.GuessNumberChromosome(FinalAns.ToString().Length);
var population = new Population(1000, 5000, chromosome);
var fitness = new Issue61.GuessNumberFitness(FinalAns);
var selection = new EliteSelection();
var crossover = new AlternatingPositionCrossover();
var mutation = new ReverseSequenceMutation();
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.MutationProbability = 0.2f;
ga.CrossoverProbability = 0.75f;
ga.Termination = new OrTermination(
new FitnessThresholdTermination(1.0),
new FitnessStagnationTermination(200),
new GenerationNumberTermination(1000));
ga.Population.GenerationStrategy = new TrackingGenerationStrategy();
ga.Start();
foreach (var gen in ga.Population.Generations)
{
foreach (var chromossome in gen.Chromosomes)
{
// Asserts if AlternatingPositionCrossover generated only ordered chromossomes.
Assert.AreEqual(chromosome.Length, chromosome.GetGenes().Distinct().Count());
}
}
}
public void ItCanPerformCrossover()
{
var one = GATestHelper.GetNumericChromosomeOne();
var two = GATestHelper.GetNumericChromosomeTwo();
var cc = new AlternatingPositionCrossover();
var child = cc.Execute(one, two, GATestHelper.GetTravelingSalesmanDefaultConfiguration());
Assert.AreEqual("1,5,2,4,3,6,7", string.Join(",", (IEnumerable <Gene>)child.Genes));
}
public void Cross_TspChromosome_Child()
{
var target = new AlternatingPositionCrossover();
var chromosome1 = new TspChromosome(100);
var chromosome2 = new TspChromosome(100);
var actual = target.Cross(new TspChromosome[] { chromosome1, chromosome2 });
Assert.AreEqual(2, actual.Count);
CollectionAssert.AllItemsAreUnique(chromosome1.GetGenes());
CollectionAssert.AllItemsAreUnique(chromosome2.GetGenes());
}
public void GA_WithAlternatingPositionCrossover_Evolve()
{
var chromosome = new TspChromosome(50);
var population = new Population(50, 50, chromosome)
{
GenerationStrategy = new TrackingGenerationStrategy()
};
var fitness = new TspFitness(chromosome.Length, 0, 1000, 0, 1000);
var crossover = new AlternatingPositionCrossover();
var ga = new GeneticAlgorithm(population, fitness, new EliteSelection(), crossover, new ReverseSequenceMutation())
{
Termination = new GenerationNumberTermination(100)
};
ga.Start();
Assert.Less(
population.Generations.First().BestChromosome.Fitness.Value,
population.Generations.Last().BestChromosome.Fitness.Value);
}
public void Cross_ParentWithNoOrderedGenes_Exception()
{
var target = new AlternatingPositionCrossover();
var chromosome1 = Substitute.For <ChromosomeBase>(8);
chromosome1.ReplaceGenes(0, new Gene[] {
new Gene(8),
new Gene(2),
new Gene(3),
new Gene(4),
new Gene(6),
new Gene(5),
new Gene(7),
new Gene(1)
});
// 3 7 5 1 6 8 2 4
var chromosome2 = Substitute.For <ChromosomeBase>(8);
chromosome2.ReplaceGenes(0, new Gene[] {
new Gene(1),
new Gene(2),
new Gene(3),
new Gene(4),
new Gene(5),
new Gene(5),
new Gene(6),
new Gene(7)
});
Assert.Catch <CrossoverException>(() =>
{
target.Cross(new List <IChromosome>()
{
chromosome1, chromosome2
});
}, "The Alternating-position (AP) can be only used with ordered chromosomes. The specified chromosome has repeated genes.");
}
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 void Cross_DocumentationSample_Child()
{
var target = new AlternatingPositionCrossover();
// 1 2 3 4 5 6 7 8
var chromosome1 = Substitute.For <ChromosomeBase>(8);
chromosome1.ReplaceGenes(0, new Gene[] {
new Gene(1),
new Gene(2),
new Gene(3),
new Gene(4),
new Gene(5),
new Gene(6),
new Gene(7),
new Gene(8)
});
var child1 = Substitute.For <ChromosomeBase>(8);
chromosome1.CreateNew().Returns(child1);
// 3 7 5 1 6 8 2 4
var chromosome2 = Substitute.For <ChromosomeBase>(8);
chromosome2.ReplaceGenes(0, new Gene[] {
new Gene(3),
new Gene(7),
new Gene(5),
new Gene(1),
new Gene(6),
new Gene(8),
new Gene(2),
new Gene(4)
});
var child2 = Substitute.For <ChromosomeBase>(8);
chromosome2.CreateNew().Returns(child2);
var actual = target.Cross(new List <IChromosome>()
{
chromosome1, chromosome2
});
Assert.AreEqual(2, actual.Count);
// 1 3 2 7 5 4 6 8
var actualChild = actual[0];
Assert.AreEqual(8, actualChild.Length);
Assert.AreEqual(1, actualChild.GetGene(0).Value);
Assert.AreEqual(3, actualChild.GetGene(1).Value);
Assert.AreEqual(2, actualChild.GetGene(2).Value);
Assert.AreEqual(7, actualChild.GetGene(3).Value);
Assert.AreEqual(5, actualChild.GetGene(4).Value);
Assert.AreEqual(4, actualChild.GetGene(5).Value);
Assert.AreEqual(6, actualChild.GetGene(6).Value);
Assert.AreEqual(8, actualChild.GetGene(7).Value);
// 3 1 7 2 5 4 6 8
actualChild = actual[1];
Assert.AreEqual(8, actualChild.Length);
Assert.AreEqual(3, actualChild.GetGene(0).Value);
Assert.AreEqual(1, actualChild.GetGene(1).Value);
Assert.AreEqual(7, actualChild.GetGene(2).Value);
Assert.AreEqual(2, actualChild.GetGene(3).Value);
Assert.AreEqual(5, actualChild.GetGene(4).Value);
Assert.AreEqual(4, actualChild.GetGene(5).Value);
Assert.AreEqual(6, actualChild.GetGene(6).Value);
Assert.AreEqual(8, actualChild.GetGene(7).Value);
}
public IList <IChromosome> AlternatingPosition()
{
var target = new AlternatingPositionCrossover();
return(target.Cross(CreateTwoParents()));
}