public void PositionBasedCrossoverApplyTest()
{
TestRandom random = new TestRandom();
Permutation parent1, parent2, expected, actual;
// The following test is based on an example from Larranaga, 1999. Genetic Algorithms for the Traveling Salesman Problem.
random.Reset();
random.IntNumbers = new int[] { 3, 1, 2, 5 };
parent1 = new Permutation(PermutationTypes.RelativeUndirected, new int[] { 0, 1, 2, 3, 4, 5, 6, 7 });
Assert.IsTrue(parent1.Validate());
parent2 = new Permutation(PermutationTypes.RelativeUndirected, new int[] { 1, 3, 5, 7, 6, 4, 2, 0 });
Assert.IsTrue(parent2.Validate());
expected = new Permutation(PermutationTypes.RelativeUndirected, new int[] { 0, 3, 5, 1, 2, 4, 6, 7 });
Assert.IsTrue(expected.Validate());
actual = PositionBasedCrossover.Apply(random, parent1, parent2);
Assert.IsTrue(actual.Validate());
Assert.IsTrue(Auxiliary.PermutationIsEqualByPosition(expected, actual));
// perform a test when two permutations are of unequal length
random.Reset();
bool exceptionFired = false;
try {
PositionBasedCrossover.Apply(random, new Permutation(PermutationTypes.RelativeUndirected, 8), new Permutation(PermutationTypes.RelativeUndirected, 6));
}
catch (System.ArgumentException) {
exceptionFired = true;
}
Assert.IsTrue(exceptionFired);
}
public void Cross_ParentWithNoOrderedGenes_Exception()
{
var target = new PositionBasedCrossover();
var chromosome1 = MockRepository.GenerateStub <ChromosomeBase>(10);
chromosome1.ReplaceGenes(0, new Gene[] {
new Gene(8),
new Gene(4),
new Gene(7),
new Gene(3),
new Gene(6),
new Gene(2),
new Gene(5),
new Gene(1),
new Gene(9),
new Gene(0)
});
chromosome1.Expect(c => c.CreateNew()).Return(MockRepository.GenerateStub <ChromosomeBase>(10));
var chromosome2 = MockRepository.GenerateStub <ChromosomeBase>(10);
chromosome2.ReplaceGenes(0, new Gene[]
{
new Gene(0),
new Gene(1),
new Gene(2),
new Gene(3),
new Gene(5),
new Gene(5),
new Gene(6),
new Gene(7),
new Gene(8),
new Gene(9),
});
chromosome2.Expect(c => c.CreateNew()).Return(MockRepository.GenerateStub <ChromosomeBase>(10));
ExceptionAssert.IsThrowing(new CrossoverException(target, "The Position-based Crossover (POS) can be only used with ordered chromosomes. The specified chromosome has repeated genes."), () =>
{
target.Cross(new List <IChromosome>()
{
chromosome1, chromosome2
});
});
}
public void Cross_ParentWithNoOrderedGenes_Exception()
{
var target = new PositionBasedCrossover();
var chromosome1 = Substitute.For <ChromosomeBase>(10);
chromosome1.ReplaceGenes(0, new Gene[] {
new Gene(8),
new Gene(4),
new Gene(7),
new Gene(3),
new Gene(6),
new Gene(2),
new Gene(5),
new Gene(1),
new Gene(9),
new Gene(0)
});
chromosome1.CreateNew().Returns(Substitute.For <ChromosomeBase>(10));
var chromosome2 = Substitute.For <ChromosomeBase>(10);
chromosome2.ReplaceGenes(0, new Gene[]
{
new Gene(0),
new Gene(1),
new Gene(2),
new Gene(3),
new Gene(5),
new Gene(5),
new Gene(6),
new Gene(7),
new Gene(8),
new Gene(9),
});
chromosome2.CreateNew().Returns(Substitute.For <ChromosomeBase>(10));
Assert.Catch <CrossoverException>(() =>
{
target.Cross(new List <IChromosome>()
{
chromosome1, chromosome2
});
}, "The Position-based Crossover (POS) 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();
});
}
private static IAlgoritmo CriaAlgoritmoGenetico(Dictionary <string, string[]> dict, List <string> flat, Problema problema)
{
int populacaoMin, populacaoMax;
IPopulation population;
ISelection selection;
ICrossover crossover;
IMutation mutation;
ITermination termination;
IReinsertion reinsertion;
float crossoverProbability, mutationProbability;
var p = dict.ValueOrDefault("p", "50,100").Split(new[] { ',' });
if (p.Length != 2 || !int.TryParse(p[0], out populacaoMin) || !int.TryParse(p[1], out populacaoMax))
{
throw new ArgumentException("Faixa de população inválida.");
}
population = new Population(populacaoMin, populacaoMax, new CromossomoViajante(problema.Mapa.Locais.Count));
switch (dict.ValueOrDefault("s", "t"))
{
case "e":
selection = new EliteSelection();
break;
case "r":
selection = new RouletteWheelSelection();
break;
case "s":
selection = new StochasticUniversalSamplingSelection();
break;
case "t":
selection = new TournamentSelection();
break;
default:
throw new ArgumentException("Seleção inválida.");
}
switch (dict.ValueOrDefault("c", "o"))
{
case "s":
crossover = new CutAndSpliceCrossover();
break;
case "c":
crossover = new CycleCrossover();
break;
case "o":
crossover = new OrderedCrossover();
break;
case "ob":
crossover = new OrderBasedCrossover();
break;
case "op":
crossover = new OnePointCrossover();
break;
case "pm":
crossover = new PartiallyMappedCrossover();
break;
case "p":
crossover = new PositionBasedCrossover();
break;
case "tpa":
crossover = new ThreeParentCrossover();
break;
case "tp":
crossover = new TwoPointCrossover();
break;
case "u":
crossover = new UniformCrossover();
break;
default:
throw new ArgumentException("Crossover inválido.");
}
switch (dict.ValueOrDefault("m", "r"))
{
case "d":
mutation = new DisplacementMutation();
break;
case "f":
mutation = new FlipBitMutation();
break;
case "i":
mutation = new InsertionMutation();
break;
case "s":
mutation = new PartialShuffleMutation();
break;
case "r":
mutation = new ReverseSequenceMutation();
break;
case "t":
mutation = new TworsMutation();
break;
case "u":
mutation = new UniformMutation();
break;
default:
throw new ArgumentException("Mutação inválida.");
}
switch (dict.ValueOrDefault("t", "s"))
{
case "s":
termination = new FitnessStagnationTermination();
break;
case "t":
termination = new FitnessThresholdTermination();
break;
case "g":
termination = new GenerationNumberTermination();
break;
default:
throw new ArgumentException("Terminação inválida.");
}
switch (dict.ValueOrDefault("e", "e"))
{
case "e":
reinsertion = new ElitistReinsertion();
break;
case "p":
reinsertion = new PureReinsertion();
break;
case "u":
reinsertion = new UniformReinsertion();
break;
default:
throw new ArgumentException("Reinserção inválida.");
}
if (!float.TryParse(dict.ValueOrDefault("cp", "0,75"), out crossoverProbability))
{
throw new ArgumentException("Probabilidade de crossover inválida.");
}
if (!float.TryParse(dict.ValueOrDefault("mp", "0,25"), out mutationProbability))
{
throw new ArgumentException("Probabilidade de mutação inválida.");
}
return(new AlgoritmoGenetico(problema, population, selection, crossover, crossoverProbability, mutation, mutationProbability, termination, reinsertion));
}
public void Cross_ParentsWith8Genes_Cross()
{
var target = new PositionBasedCrossover();
// 1 2 3 4 5 6 7 8
var chromosome1 = MockRepository.GenerateStub <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)
});
chromosome1.Expect(c => c.CreateNew()).Return(MockRepository.GenerateStub <ChromosomeBase>(8));
// 2 4 6 8 7 5 3 1
var chromosome2 = MockRepository.GenerateStub <ChromosomeBase>(8);
chromosome2.ReplaceGenes(0, new Gene[]
{
new Gene(2),
new Gene(4),
new Gene(6),
new Gene(8),
new Gene(7),
new Gene(5),
new Gene(3),
new Gene(1)
});
chromosome2.Expect(c => c.CreateNew()).Return(MockRepository.GenerateStub <ChromosomeBase>(8));
// Child one: 1 4 6 2 3 5 7 8
// Child two: 4 2 3 8 7 6 5 1
var rnd = MockRepository.GenerateMock <IRandomization>();
rnd.Expect(r => r.GetInt(1, 7)).Return(3);
rnd.Expect(r => r.GetUniqueInts(3, 0, 8)).Return(new int[] { 1, 2, 5 });
RandomizationProvider.Current = rnd;
IList <IChromosome> actual = null;;
TimeAssert.LessThan(40, () =>
{
actual = target.Cross(new List <IChromosome>()
{
chromosome1, chromosome2
});
});
Assert.AreEqual(2, actual.Count);
var childOne = actual [0];
var childTwo = actual [1];
Assert.AreEqual(8, childOne.Length);
Assert.AreEqual(8, childTwo.Length);
Assert.AreEqual(8, childOne.GetGenes().Distinct().Count());
Assert.AreEqual(8, childTwo.GetGenes().Distinct().Count());
Assert.AreEqual(1, childOne.GetGene(0).Value);
Assert.AreEqual(4, childOne.GetGene(1).Value);
Assert.AreEqual(6, childOne.GetGene(2).Value);
Assert.AreEqual(2, childOne.GetGene(3).Value);
Assert.AreEqual(3, childOne.GetGene(4).Value);
Assert.AreEqual(5, childOne.GetGene(5).Value);
Assert.AreEqual(7, childOne.GetGene(6).Value);
Assert.AreEqual(8, childOne.GetGene(7).Value);
Assert.AreEqual(4, childTwo.GetGene(0).Value);
Assert.AreEqual(2, childTwo.GetGene(1).Value);
Assert.AreEqual(3, childTwo.GetGene(2).Value);
Assert.AreEqual(8, childTwo.GetGene(3).Value);
Assert.AreEqual(7, childTwo.GetGene(4).Value);
Assert.AreEqual(6, childTwo.GetGene(5).Value);
Assert.AreEqual(5, childTwo.GetGene(6).Value);
Assert.AreEqual(1, childTwo.GetGene(7).Value);
}
public void Cross_ParentsWith6Genes_Cross()
{
var target = new PositionBasedCrossover();
// 1 5 4 0 3 2
var chromosome1 = MockRepository.GenerateStub <ChromosomeBase>(6);
chromosome1.ReplaceGenes(0, new Gene[] {
new Gene(1),
new Gene(5),
new Gene(4),
new Gene(0),
new Gene(3),
new Gene(2)
});
chromosome1.Expect(c => c.CreateNew()).Return(MockRepository.GenerateStub <ChromosomeBase>(6));
// 2 3 5 0 1 4
var chromosome2 = MockRepository.GenerateStub <ChromosomeBase>(6);
chromosome2.ReplaceGenes(0, new Gene[]
{
new Gene(2),
new Gene(3),
new Gene(5),
new Gene(0),
new Gene(1),
new Gene(4)
});
chromosome2.Expect(c => c.CreateNew()).Return(MockRepository.GenerateStub <ChromosomeBase>(6));
// Child one: 4 3 5 0 1 2
// Child two: 2 5 4 0 3 1
var rnd = MockRepository.GenerateMock <IRandomization>();
rnd.Expect(r => r.GetInt(1, 5)).Return(3);
rnd.Expect(r => r.GetUniqueInts(3, 0, 6)).Return(new int[] { 2, 4, 3 });
RandomizationProvider.Current = rnd;
IList <IChromosome> actual = null;;
TimeAssert.LessThan(40, () =>
{
actual = target.Cross(new List <IChromosome>()
{
chromosome1, chromosome2
});
});
Assert.AreEqual(2, actual.Count);
var childOne = actual[0];
var childTwo = actual[1];
Assert.AreEqual(6, childOne.Length);
Assert.AreEqual(6, childTwo.Length);
Assert.AreEqual(6, childOne.GetGenes().Distinct().Count());
Assert.AreEqual(6, childTwo.GetGenes().Distinct().Count());
Assert.AreEqual(4, childOne.GetGene(0).Value);
Assert.AreEqual(3, childOne.GetGene(1).Value);
Assert.AreEqual(5, childOne.GetGene(2).Value);
Assert.AreEqual(0, childOne.GetGene(3).Value);
Assert.AreEqual(1, childOne.GetGene(4).Value);
Assert.AreEqual(2, childOne.GetGene(5).Value);
Assert.AreEqual(2, childTwo.GetGene(0).Value);
Assert.AreEqual(5, childTwo.GetGene(1).Value);
Assert.AreEqual(4, childTwo.GetGene(2).Value);
Assert.AreEqual(0, childTwo.GetGene(3).Value);
Assert.AreEqual(3, childTwo.GetGene(4).Value);
Assert.AreEqual(1, childTwo.GetGene(5).Value);
}
public void Cross_ParentsWith8Genes_Cross()
{
var target = new PositionBasedCrossover();
// 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)
});
chromosome1.CreateNew().Returns(Substitute.For <ChromosomeBase>(8));
// 2 4 6 8 7 5 3 1
var chromosome2 = Substitute.For <ChromosomeBase>(8);
chromosome2.ReplaceGenes(0, new Gene[]
{
new Gene(2),
new Gene(4),
new Gene(6),
new Gene(8),
new Gene(7),
new Gene(5),
new Gene(3),
new Gene(1)
});
chromosome2.CreateNew().Returns(Substitute.For <ChromosomeBase>(8));
// Child one: 1 4 6 2 3 5 7 8
// Child two: 4 2 3 8 7 6 5 1
var rnd = Substitute.For <IRandomization>();
rnd.GetInt(1, 7).Returns(3);
rnd.GetUniqueInts(3, 0, 8).Returns(new int[] { 1, 2, 5 });
RandomizationProvider.Current = rnd;
var actual = target.Cross(new List <IChromosome>()
{
chromosome1, chromosome2
});
Assert.AreEqual(2, actual.Count);
var childOne = actual [0];
var childTwo = actual [1];
Assert.AreEqual(8, childOne.Length);
Assert.AreEqual(8, childTwo.Length);
Assert.AreEqual(8, childOne.GetGenes().Distinct().Count());
Assert.AreEqual(8, childTwo.GetGenes().Distinct().Count());
Assert.AreEqual(1, childOne.GetGene(0).Value);
Assert.AreEqual(4, childOne.GetGene(1).Value);
Assert.AreEqual(6, childOne.GetGene(2).Value);
Assert.AreEqual(2, childOne.GetGene(3).Value);
Assert.AreEqual(3, childOne.GetGene(4).Value);
Assert.AreEqual(5, childOne.GetGene(5).Value);
Assert.AreEqual(7, childOne.GetGene(6).Value);
Assert.AreEqual(8, childOne.GetGene(7).Value);
Assert.AreEqual(4, childTwo.GetGene(0).Value);
Assert.AreEqual(2, childTwo.GetGene(1).Value);
Assert.AreEqual(3, childTwo.GetGene(2).Value);
Assert.AreEqual(8, childTwo.GetGene(3).Value);
Assert.AreEqual(7, childTwo.GetGene(4).Value);
Assert.AreEqual(6, childTwo.GetGene(5).Value);
Assert.AreEqual(5, childTwo.GetGene(6).Value);
Assert.AreEqual(1, childTwo.GetGene(7).Value);
}
public void Cross_ParentsWith6Genes_Cross()
{
var target = new PositionBasedCrossover();
// 1 5 4 0 3 2
var chromosome1 = Substitute.For <ChromosomeBase>(6);
chromosome1.ReplaceGenes(0, new Gene[] {
new Gene(1),
new Gene(5),
new Gene(4),
new Gene(0),
new Gene(3),
new Gene(2)
});
chromosome1.CreateNew().Returns(Substitute.For <ChromosomeBase>(6));
// 2 3 5 0 1 4
var chromosome2 = Substitute.For <ChromosomeBase>(6);
chromosome2.ReplaceGenes(0, new Gene[]
{
new Gene(2),
new Gene(3),
new Gene(5),
new Gene(0),
new Gene(1),
new Gene(4)
});
chromosome2.CreateNew().Returns(Substitute.For <ChromosomeBase>(6));
// Child one: 4 3 5 0 1 2
// Child two: 2 5 4 0 3 1
var rnd = Substitute.For <IRandomization>();
rnd.GetInt(1, 5).Returns(3);
rnd.GetUniqueInts(3, 0, 6).Returns(new int[] { 2, 4, 3 });
RandomizationProvider.Current = rnd;
var actual = target.Cross(new List <IChromosome>()
{
chromosome1, chromosome2
});
Assert.AreEqual(2, actual.Count);
var childOne = actual[0];
var childTwo = actual[1];
Assert.AreEqual(6, childOne.Length);
Assert.AreEqual(6, childTwo.Length);
Assert.AreEqual(6, childOne.GetGenes().Distinct().Count());
Assert.AreEqual(6, childTwo.GetGenes().Distinct().Count());
Assert.AreEqual(4, childOne.GetGene(0).Value);
Assert.AreEqual(3, childOne.GetGene(1).Value);
Assert.AreEqual(5, childOne.GetGene(2).Value);
Assert.AreEqual(0, childOne.GetGene(3).Value);
Assert.AreEqual(1, childOne.GetGene(4).Value);
Assert.AreEqual(2, childOne.GetGene(5).Value);
Assert.AreEqual(2, childTwo.GetGene(0).Value);
Assert.AreEqual(5, childTwo.GetGene(1).Value);
Assert.AreEqual(4, childTwo.GetGene(2).Value);
Assert.AreEqual(0, childTwo.GetGene(3).Value);
Assert.AreEqual(3, childTwo.GetGene(4).Value);
Assert.AreEqual(1, childTwo.GetGene(5).Value);
}
public IList <IChromosome> PositionBasedCrossover()
{
var target = new PositionBasedCrossover();
return(target.Cross(CreateTwoParents()));
}
