public void ItHasAValidConstructor()
{
var parentSelection = new StochasticUniversalSamplingSelection();
parentSelection.Setup(_pool, GATestHelper.GetTravelingSalesmanDefaultConfiguration());
parentSelection.GetParents();
}
public ISelection StochasticUniversalSampling()
{
var target = new StochasticUniversalSamplingSelection();
target.SelectChromosomes(_chromosomesNumber, _generation);
return(target);
}
public void SelectChromosomes_NullGeneration_Exception()
{
var target = new StochasticUniversalSamplingSelection();
ExceptionAssert.IsThrowing(new ArgumentNullException("generation"), () =>
{
target.SelectChromosomes(2, null);
});
}
public void ItCanGetAValidParent()
{
var parentSelection = new StochasticUniversalSamplingSelection();
parentSelection.Setup(_pool, GATestHelper.GetTravelingSalesmanDefaultConfiguration());
var parent = parentSelection.GetParent(0.22);
Assert.IsNotNull(parent);
}
public void SelectChromosomes_NullGeneration_Exception()
{
var target = new StochasticUniversalSamplingSelection();
var actual = Assert.Catch <ArgumentNullException>(() =>
{
target.SelectChromosomes(2, null);
});
Assert.AreEqual("generation", actual.ParamName);
}
public void SelectChromosomes_InvalidNumber_Exception()
{
var target = new StochasticUniversalSamplingSelection();
ExceptionAssert.IsThrowing(new ArgumentOutOfRangeException("number", "The number of selected chromosomes should be at least 2."), () =>
{
target.SelectChromosomes(-1, null);
});
ExceptionAssert.IsThrowing(new ArgumentOutOfRangeException("number", "The number of selected chromosomes should be at least 2."), () =>
{
target.SelectChromosomes(0, null);
});
ExceptionAssert.IsThrowing(new ArgumentOutOfRangeException("number", "The number of selected chromosomes should be at least 2."), () =>
{
target.SelectChromosomes(1, null);
});
}
public void SelectChromosomes_InvalidNumber_Exception()
{
var target = new StochasticUniversalSamplingSelection();
Assert.Catch <ArgumentOutOfRangeException>(() =>
{
target.SelectChromosomes(-1, null);
}, "The number of selected chromosomes should be at least 2.");
Assert.Catch <ArgumentOutOfRangeException>(() =>
{
target.SelectChromosomes(0, null);
}, "The number of selected chromosomes should be at least 2.");
Assert.Catch <ArgumentOutOfRangeException>(() =>
{
target.SelectChromosomes(1, null);
}, "The number of selected chromosomes should be at least 2.");
}
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 SelectChromosomes_Generation_ChromosomesSelected()
{
var target = new StochasticUniversalSamplingSelection();
var c1 = new ChromosomeStub();
c1.Fitness = 0.1;
var c2 = new ChromosomeStub();
c2.Fitness = 0.5;
var c3 = new ChromosomeStub();
c3.Fitness = 0;
var c4 = new ChromosomeStub();
c4.Fitness = 0.7;
var generation = new Generation(1, new List <IChromosome>()
{
c1, c2, c3, c4
});
// Fitness sum: 0.1 + 0.5 + 0 + 0.7 = 1.3
// c1: 8% = 0.08
// c2: 38% = 0.46
// c3: 0% = 0.46
// c4: 54% = 1.00
var rnd = MockRepository.GenerateMock <IRandomization>();
rnd.Expect(r => r.GetDouble()).Return(0.3);
RandomizationProvider.Current = rnd;
// Step size 1/2 = 0.5
var actual = target.SelectChromosomes(2, generation);
Assert.AreEqual(2, actual.Count);
Assert.AreEqual(c2.Fitness, actual[0].Fitness); // 0.3
Assert.AreEqual(c4.Fitness, actual[1].Fitness); // 0.8
// Step size 1/3 = 0.33
actual = target.SelectChromosomes(3, generation);
Assert.AreEqual(3, actual.Count);
Assert.AreEqual(c2.Fitness, actual[0].Fitness); // 0.3
Assert.AreEqual(c4.Fitness, actual[1].Fitness); // 0.63
Assert.AreEqual(c4.Fitness, actual[2].Fitness); // 0.96
// Step size 1/4 = 0.25
actual = target.SelectChromosomes(4, generation);
Assert.AreEqual(4, actual.Count);
Assert.AreEqual(c2.Fitness, actual[0].Fitness); // 0.3
Assert.AreEqual(c4.Fitness, actual[1].Fitness); // 0.55
Assert.AreEqual(c4.Fitness, actual[2].Fitness); // 0.80
Assert.AreEqual(c1.Fitness, actual[3].Fitness); // 0.05
// Step size 1/5 = 0.20
actual = target.SelectChromosomes(5, generation);
Assert.AreEqual(5, actual.Count);
Assert.AreEqual(c2.Fitness, actual[0].Fitness); // 0.3
Assert.AreEqual(c4.Fitness, actual[1].Fitness); // 0.5
Assert.AreEqual(c4.Fitness, actual[2].Fitness); // 0.7
Assert.AreEqual(c4.Fitness, actual[3].Fitness); // 0.9
Assert.AreEqual(c2.Fitness, actual[4].Fitness); // 0.1
}
/// <summary>
/// Loads presetsProfile into service.
/// </summary>
/// <exception cref="T:System.InvalidOperationException">Thrown when an attempt to load presetsProfile twice is made.</exception>
public void AddGeneticAlgorithm(GADefinition definition, Guid Key)
{
var chromosome = new FloatingPointChromosome(
minValue: new double[] { 0, 0, 0, 0 },
maxValue: new double[] { _maxWidth, _maxHeight, _maxWidth, _maxHeight },
totalBits: new int[] { 20, 20, 20, 20 },
fractionDigits: new int[] { 0, 0, 0, 0 }, geneValues: _geneValues);
ICrossover gaCrossover = default;
switch (definition.Crossover)
{
case Crossovers.Uniform:
gaCrossover = new UniformCrossover(mixProbability: 0.5f);
break;
case Crossovers.OnePoint:
gaCrossover = new OnePointCrossover(swapPointIndex: 40);
break;
case Crossovers.ThreeParent:
gaCrossover = new ThreeParentCrossover();
break;
default:
throw new ArgumentOutOfRangeException(paramName: nameof(definition.Crossover),
actualValue: definition.Crossover, message: "Crossover has wrong value");
}
ISelection gaSelection = default;
switch (definition.Selection)
{
case Selections.Elite:
gaSelection = new EliteSelection();
break;
case Selections.Roulette:
gaSelection = new RouletteWheelSelection();
break;
case Selections.Tournament:
gaSelection = new TournamentSelection(
size: decimal.ToInt32(d: decimal.Multiply(definition.Population, new decimal(0.2f))));
break;
case Selections.StohasticUniversalSampling:
gaSelection = new StochasticUniversalSamplingSelection();
break;
default:
throw new ArgumentOutOfRangeException(paramName: nameof(definition.Selection),
actualValue: definition.Selection, message: "Selection has wrong value");
}
var gaMutation = new UniformMutation(true);
var gaPopulation = new Population(minSize: definition.Population,
maxSize: definition.Population, adamChromosome: chromosome);
var ga = new GeneticAlgorithm(population: gaPopulation,
fitness: new EuclideanDistanceFitness(), selection: gaSelection,
crossover: gaCrossover, mutation: gaMutation);
ga.MutationProbability = (float)definition.Mutation;
ga.GenerationRan += GeneticAlgorithmOnGenerationRan;
ga.Termination =
new FitnessStagnationTermination(expectedStagnantGenerationsNumber: 5);
_geneticAlgorithms.Add(key: Key, value: ga);
}