public void Cross_ParentsWithDiffLength_Cross()
{
var target = new CutAndSpliceCrossover();
var chromosome1 = MockRepository.GenerateStub <ChromosomeBase>(4);
chromosome1.ReplaceGenes(0, new Gene[]
{
new Gene(1),
new Gene(2),
new Gene(3),
new Gene(4),
});
chromosome1.Expect(c => c.CreateNew()).Return(MockRepository.GenerateStub <ChromosomeBase>(4));
var chromosome2 = MockRepository.GenerateStub <ChromosomeBase>(5);
chromosome2.ReplaceGenes(0, new Gene[]
{
new Gene(5),
new Gene(6),
new Gene(7),
new Gene(8),
new Gene(9),
});
chromosome2.Expect(c => c.CreateNew()).Return(MockRepository.GenerateStub <ChromosomeBase>(5));
var repository = new MockRepository();
var rnd = repository.StrictMock <IRandomization>();
using (repository.Ordered())
{
rnd.Expect(r => r.GetInt(1, 4)).Return(2);
rnd.Expect(r => r.GetInt(1, 5)).Return(2);
}
repository.ReplayAll();
RandomizationProvider.Current = rnd;
var actual = target.Cross(new List <IChromosome>()
{
chromosome1, chromosome2
});
Assert.AreEqual(2, actual.Count);
Assert.AreEqual(5, actual[0].Length);
Assert.AreEqual(4, actual[1].Length);
Assert.AreEqual(1, actual[0].GetGene(0).Value);
Assert.AreEqual(2, actual[0].GetGene(1).Value);
Assert.AreEqual(3, actual[0].GetGene(2).Value);
Assert.AreEqual(8, actual[0].GetGene(3).Value);
Assert.AreEqual(9, actual[0].GetGene(4).Value);
Assert.AreEqual(5, actual[1].GetGene(0).Value);
Assert.AreEqual(6, actual[1].GetGene(1).Value);
Assert.AreEqual(7, actual[1].GetGene(2).Value);
Assert.AreEqual(4, actual[1].GetGene(3).Value);
}
public void Cross_ParentsWithDiffLength_Cross()
{
var target = new CutAndSpliceCrossover();
var chromosome1 = Substitute.ForPartsOf <ChromosomeBase>(4);
chromosome1.ReplaceGenes(0, new Gene[]
{
new Gene(1),
new Gene(2),
new Gene(3),
new Gene(4),
});
chromosome1.CreateNew().Returns(Substitute.ForPartsOf <ChromosomeBase>(4));
var chromosome2 = Substitute.ForPartsOf <ChromosomeBase>(5);
chromosome2.ReplaceGenes(0, new Gene[]
{
new Gene(5),
new Gene(6),
new Gene(7),
new Gene(8),
new Gene(9),
});
chromosome2.CreateNew().Returns(Substitute.ForPartsOf <ChromosomeBase>(5));
var rnd = Substitute.For <IRandomization>();
rnd.GetInt(1, 4).Returns(2);
rnd.GetInt(1, 5).Returns(2);
RandomizationProvider.Current = rnd;
var actual = target.Cross(new List <IChromosome>()
{
chromosome1, chromosome2
});
Assert.AreEqual(2, actual.Count);
Assert.AreEqual(5, actual[0].Length);
Assert.AreEqual(4, actual[1].Length);
Assert.AreEqual(1, actual[0].GetGene(0).Value);
Assert.AreEqual(2, actual[0].GetGene(1).Value);
Assert.AreEqual(3, actual[0].GetGene(2).Value);
Assert.AreEqual(8, actual[0].GetGene(3).Value);
Assert.AreEqual(9, actual[0].GetGene(4).Value);
Assert.AreEqual(5, actual[1].GetGene(0).Value);
Assert.AreEqual(6, actual[1].GetGene(1).Value);
Assert.AreEqual(7, actual[1].GetGene(2).Value);
Assert.AreEqual(4, actual[1].GetGene(3).Value);
}
static void Main(string[] args)
{
DataModel data_model = new DataModel();
InputReader input_reader = new InputReader();
ReadWatch.Start();
// input_reader.ReadDataFromFile("data/me_at_the_zoo.in");
input_reader.ReadDataFromFile("data/videos_worth_spreading.in");
// input_reader.ReadDataFromFile("data/trending_today.in");
// input_reader.ReadDataFromFile("data/kittens.in");
ReadWatch.Stop();
Console.WriteLine($"{ReadWatch.ElapsedMilliseconds}");
var selection = new EliteSelection();
var crossover = new CutAndSpliceCrossover();
var mutation = new AddGenesMutation(10);
var fitness = new Fitness();
var chromosome = new Chromosome(3000);
var population = new Population(1000, 1000, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.MutationProbability = 0.25f;
ga.Termination = new GenerationNumberTermination(250);
int i = 0;
ga.GenerationRan += (_, __) => {
var bestC = ga.BestChromosome as Chromosome;
SaveToFile.Save(bestC, @"data\output");
Console.WriteLine($@"G.num.: {i++}, Fitness: {ga.BestChromosome.Fitness}, Lenght: {ga.BestChromosome.Length}, generation time = {GenerationWatch.ElapsedMilliseconds}, Fitness time = {WholeFitnessWatch.ElapsedMilliseconds}, Fitness percentage: {((float)WholeFitnessWatch.ElapsedMilliseconds/(float)GenerationWatch.ElapsedMilliseconds)}");
GenerationWatch.Restart();
WholeFitnessWatch.Reset();
};
Console.WriteLine("GA running...");
GenerationWatch.Start();
ga.Start();
Console.WriteLine($"GA done in {ga.GenerationsNumber} generations.");
var bestChromosome = ga.BestChromosome as Chromosome;
Console.WriteLine($"Best solution found has fitness: {bestChromosome.Fitness}.");
Console.WriteLine("Done");
}
public void Run()
{
var selection = new InitialStrategySelection();
var crossover = new CutAndSpliceCrossover();
var mutation = new TworsMutation();
var fitness = new InitialStrategyFitness();
var chromosome = new InitialStrategyChromosome();
var population = new Population(50, 70, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new FitnessStagnationTermination(20);
Console.WriteLine("GA running...");
ga.Start();
Console.WriteLine("Best solution found has {0} fitness.", ga.BestChromosome.Fitness);
}
public void Play2048PatternTest()
{
var selection = new EliteSelection();
var crossover = new CutAndSpliceCrossover();
var mutation = new PartialShuffleMutation();
var fitness = new ClosestToMillion();
var chromosome = new Play2048Chromosome(20, new List <string>()
{
"up", "down", "left", "right"
});
var population = new CustomPopulation(2, 4, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new GenerationNumberTermination(10);
Console.WriteLine("GA running...");
ga.GenerationRan += Ga_GenerationRan;
Console.WriteLine("Best solution found has {0} fitness was born in generation {1}", ga.BestChromosome.Fitness, bestGen);
}
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 IList <IChromosome> CutAndSpliceCrossover()
{
var target = new CutAndSpliceCrossover();
return(target.Cross(CreateTwoParents()));
}
