public void Evolve_ManyGenerations_Fast()
{
int numberOfCities = 40;
var selection = new EliteSelection();
var crossover = new OrderedCrossover();
var mutation = new TworsMutation();
var chromosome = new TspChromosome(numberOfCities);
var fitness = new TspFitness (numberOfCities, 0, 1000, 0, 1000);
var population = new Population (40, 40, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Start();
var firstDistance = ((TspChromosome)ga.Population.BestChromosome).Distance;
ga.Termination = new GenerationNumberTermination (1001);
TimeAssert.LessThan(3000, () =>
{
ga.Start();
});
var lastDistance = ((TspChromosome)ga.Population.BestChromosome).Distance;
Assert.Less(lastDistance, firstDistance);
}
public void Evolve_ManyGenerations_Fast()
{
var selection = new EliteSelection();
var crossover = new UniformCrossover();
var mutation = new UniformMutation(true);
var chromosome = new AutoConfigChromosome();
var targetChromosome = new TspChromosome(10);
var targetFitness = new TspFitness(10, 0, 100, 0, 100);
var fitness = new AutoConfigFitness(targetFitness, targetChromosome);
fitness.PopulationMinSize = 20;
fitness.PopulationMaxSize = 20;
fitness.Termination = new TimeEvolvingTermination(TimeSpan.FromSeconds(5));
var population = new Population(10, 10, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.TaskExecutor = new SmartThreadPoolTaskExecutor()
{
MinThreads = 10,
MaxThreads = 20
};
ga.Termination = new GenerationNumberTermination(2);
ga.Start();
Assert.NotNull(ga.BestChromosome);
}
public override void ConfigGA(GeneticAlgorithm ga)
{
base.ConfigGA(ga);
ga.MutationProbability = 0.4f;
ga.TerminationReached += (sender, args) =>
{
using (var collection = new MagickImageCollection())
{
var files = Directory.GetFiles(m_destFolder, "*.png");
foreach (var image in files)
{
collection.Add(image);
collection[0].AnimationDelay = 100;
}
var settings = new QuantizeSettings();
settings.Colors = 256;
collection.Quantize(settings);
collection.Optimize();
collection.Write(Path.Combine(m_destFolder, "result.gif"));
}
};
}
public static GeneticSharp.Domain.GeneticAlgorithm DefaultGeneticAlgorithm(Func <double[], double> func, Tuple <double, double>[] minmax)
{
var population = new Population(20, 40, chromosome.CreateIntChromosone(minmax));
var fitness = new FuncFitness((c) =>
{
var fc = c as FloatingPointChromosome;
var values = fc.ToFloatingPoints();
return(func(values));
});
var selection = new EliteSelection();
var crossover = new UniformCrossover(0.5f);
var mutation = new FlipBitMutation();
var ga = new GeneticSharp.Domain.GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation);
var termination = new FitnessStagnationTermination(100);
ga.Termination = termination;
Console.WriteLine("Generation: (x1, y1), (x2, y2) = distance");
return(ga);
}
public void Evolve_ManyGenerations_Fast()
{
var selection = new EliteSelection();
var crossover = new ThreeParentCrossover();
var mutation = new UniformMutation(true);
var fitness = new FunctionBuilderFitness(
new FunctionBuilderInput(
new double[] { 1, 2, 3 },
6)
,
new FunctionBuilderInput(
new double[] { 2, 3, 4 },
24)
);
var chromosome = new FunctionBuilderChromosome(fitness.AvailableOperations, 5);
var population = new Population(100, 200, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new FitnessThresholdTermination(0);
ga.Start();
var bestChromosome = ga.BestChromosome as FunctionBuilderChromosome;
Assert.AreEqual(0.0, bestChromosome.Fitness.Value);
var actual = fitness.GetFunctionResult(
bestChromosome.BuildFunction(),
new FunctionBuilderInput(new double[] { 3, 4, 5 }, 60)
);
Assert.AreEqual(60.0, actual);
}
public void Play2048PatternTest()
{
var selection = new EliteSelection();
var crossover = new TwoPointCrossover();
var mutation = new PartialShuffleMutation();
var fitness = new ClosestToMillion();
var chromosome = new Play2048Chromosome(20, new List <string>()
{
"up", "down", "left", "right"
});
var population = new Population(22, 42, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new GenerationNumberTermination(10);
Console.WriteLine("GA running...");
ga.GenerationRan += Ga_GenerationRan;
ga.Population.BestChromosomeChanged += Population_BestChromosomeChanged;
Task.Run(async() => { await Start(ga); }).Wait();
GA.GetSingleton().NextPattern = string.Empty;
Console.WriteLine((bestChromosome as Play2048Chromosome).Pattern);
Console.WriteLine("Best solution found has {0} fitness was born in generation {1}", ga.BestChromosome.Fitness, bestGen);
}
public void Play2048PatternTest2()
{
var selection = new EliteSelection();
var crossover = new TwoPointCrossover();
var mutation = new PartialShuffleMutation();
var fitness = new ClosestToMillion();
var bestChromosomeTodate = new Play2048Chromosome(20, new List <string>()
{
"up", "down", "left", "right"
});
//bestChromosomeTodate.Pattern = "";
var population = new Population(4, 5, bestChromosomeTodate);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new GenerationNumberTermination(1);
Console.WriteLine("GA running...");
ga.GenerationRan += Ga_GenerationRan;
ga.Population.BestChromosomeChanged += Population_BestChromosomeChanged;
ga.Init();
//queue up all the patterns...
Console.WriteLine((bestChromosome as Play2048Chromosome).Pattern);
Console.WriteLine("Best solution found has {0} fitness was born in generation {1}", ga.BestChromosome.Fitness, bestGen);
}
public void Evolve_ManyGenerations_Fast()
{
int movesAhead = 10;
int boardSize = 10;
var selection = new EliteSelection();
var crossover = new OrderedCrossover();
var mutation = new TworsMutation();
var chromosome = new CheckersChromosome(movesAhead, boardSize);
var fitness = new CheckersFitness(new CheckersBoard(boardSize));
var population = new Population(40, 40, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.GenerationRan += delegate
{
if (ga.Population.GenerationsNumber % 100 == 0)
{
fitness.Update(ga.Population.BestChromosome as CheckersChromosome);
}
};
ga.Start();
var firstFitness = ((CheckersChromosome)ga.Population.BestChromosome).Fitness;
ga.Termination = new GenerationNumberTermination(2001);
TimeAssert.LessThan(100000, () =>
{
ga.Start();
});
var lastFitness = ((CheckersChromosome)ga.Population.BestChromosome).Fitness;
Assert.LessOrEqual(firstFitness, lastFitness);
}
public override void ConfigGA(GeneticAlgorithm ga)
{
base.ConfigGA(ga);
ga.TaskExecutor = new SmartThreadPoolTaskExecutor()
{
MinThreads = 25,
MaxThreads = 50
};
}
public OuterWrap(params Tuple <double, double>[] minmax)
{
latestFitness = 0.0;
ga = GAFactory.MakeDefault(FunctionToOptimize, minmax);
ga.GenerationRan += NewGeneration;
measurements = SignalGenerator.GetPeriodicDefault();
NotifyOfImprovement += GeneticAlgorithm_NotifyOfImprovement;
}
public double TrainNeuralNetwork()
{
var chromosome = new KasandraChromosome(_fitness.TotalNumberOfWeights);
var population = new Population(minPopulation, maxPopulation, chromosome);
_geneticAlgorithm = new GeneticAlgorithm(population, _fitness, new EliteSelection(), new UniformCrossover(), new UniformMutation(true));
_geneticAlgorithm.MutationProbability = mutationRate;
_geneticAlgorithm.CrossoverProbability = crossoverProbabilty;
_geneticAlgorithm.Termination = new GenerationNumberTermination(maxIterations);
_geneticAlgorithm.TerminationReached += _geneticAlgorithm_TerminationReached;
_geneticAlgorithm.GenerationRan += _geneticAlgorithm_GenerationRan;
_geneticAlgorithm.Start();
return 0;
}
public GeneticAlgorithmWrapper(double maxh, double maxw)
{
latestFitness = 0.0;
var listminmax = new List <Tuple <double, double> >
{
{ new Tuple <double, double>(0, maxw) },
{ new Tuple <double, double>(0, maxw) },
{ new Tuple <double, double>(0, maxh) },
{ new Tuple <double, double>(0, maxh) }
};
ga = GAFactory.DefaultGeneticAlgorithm(FunctionToOptimise, listminmax.ToArray());
ga.GenerationRan += NewGeneration;
NotifyOfImprovement += GeneticAlgorithm_NotifyOfImprovement;
}
static void Main(string[] args)
{
var selection = new EliteSelection();
var crossover = new OnePointCrossover(0);
var mutation = new UniformMutation(true);
var fitness = new Issue1Fitness();
var chromosome = new Issue1Chromosome();
var population = new Population(50, 50, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new GenerationNumberTermination(100);
Console.WriteLine("GA running...");
ga.Start();
Console.WriteLine("GA done in {0} generations.", ga.GenerationsNumber);
var bestChromosome = ga.BestChromosome as Issue1Chromosome;
Console.WriteLine("Best solution found is X:{0}, Y:{1} with {2} fitness.", bestChromosome.X, bestChromosome.Y, bestChromosome.Fitness);
Console.ReadKey();
}
public double Evaluate(IChromosome chromosome)
{
var autoConfigChromosome = chromosome as AutoConfigChromosome;
var selection = autoConfigChromosome.Selection;
var crossover = autoConfigChromosome.Crossover;
var mutation = autoConfigChromosome.Mutation;
var population = new Population(PopulationMinSize, PopulationMaxSize, m_targetChromosome);
var ga = new GeneticAlgorithm(population, m_targetFitness, selection, crossover, mutation);
ga.Termination = Termination;
ga.TaskExecutor = TaskExecutor;
try
{
ga.Start();
}
catch (Exception)
{
return 0;
}
return ga.BestChromosome.Fitness.Value;
}
/// <summary>
/// Configure the Genetic Algorithm.
/// </summary>
/// <param name="ga">The genetic algorithm.</param>
public override void ConfigGA(GeneticAlgorithm ga)
{
ga.CrossoverProbability = 0.1f;
ga.MutationProbability = 0.4f;
ga.Reinsertion = new ElitistReinsertion();
}
static void Main(string[] args)
{
Console.ForegroundColor = ConsoleColor.DarkGreen;
Console.WriteLine("GeneticSharp - ConsoleApp");
Console.ResetColor();
Console.WriteLine("Select the sample:");
Console.WriteLine("1) TSP (Travelling Salesman Problem)");
Console.WriteLine("2) Ghostwriter");
var sampleNumber = Console.ReadLine();
ISampleController sampleController = null;
switch (sampleNumber)
{
case "1":
sampleController = new TspSampleController(20);
break;
case "2":
sampleController = new GhostwriterSampleController();
break;
default:
return;
}
var selection = new EliteSelection();
var crossover = new UniformCrossover();
var mutation = new UniformMutation(true);
var fitness = sampleController.CreateFitness();
var population = new Population(50, 70, sampleController.CreateChromosome());
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.MutationProbability = 0.4f;
ga.Termination = new FitnessStagnationTermination(100);
ga.TaskExecutor = new SmartThreadPoolTaskExecutor()
{
MinThreads = 25,
MaxThreads = 50
};
ga.GenerationRan += delegate
{
Console.CursorLeft = 0;
Console.CursorTop = 5;
var bestChromosome = ga.Population.BestChromosome;
Console.WriteLine("Generations: {0}", ga.Population.GenerationsNumber);
Console.WriteLine("Fitness: {0:n4}", bestChromosome.Fitness);
Console.WriteLine("Time: {0}", ga.TimeEvolving);
sampleController.Draw(bestChromosome);
};
try
{
ga.Start();
}
catch (Exception ex)
{
Console.ForegroundColor = ConsoleColor.DarkRed;
Console.WriteLine();
Console.WriteLine("Error: {0}", ex.Message);
Console.ResetColor();
Console.ReadKey();
return;
}
Console.ForegroundColor = ConsoleColor.DarkGreen;
Console.WriteLine();
Console.WriteLine("Evolved.");
Console.ResetColor();
Console.ReadKey();
}
private string Start(GeneticAlgorithm ga)
{
ga.Start();
return(string.Empty);
}
/// <summary>
/// Configure the Genetic Algorithm.
/// </summary>
/// <param name="ga">The genetic algorithm.</param>
public virtual void ConfigGA(GeneticAlgorithm ga)
{
GA = ga;
}
private static void Run()
{
Console.SetError(TextWriter.Null);
Console.Clear();
Console.ForegroundColor = ConsoleColor.DarkGreen;
Console.WriteLine("GeneticSharp - ConsoleApp");
Console.ResetColor();
Console.WriteLine("Select the sample:");
var sampleNames = TypeHelper.GetDisplayNamesByInterface<ISampleController>();
for (int i = 0; i < sampleNames.Count; i++)
{
Console.WriteLine("{0}) {1}", i + 1, sampleNames[i]);
}
int sampleNumber = 0;
string selectedSampleName = string.Empty;
try
{
sampleNumber = Convert.ToInt32(Console.ReadLine());
selectedSampleName = sampleNames[sampleNumber - 1];
}
catch (Exception)
{
Console.WriteLine("Invalid option.");
}
var sampleController = TypeHelper.CreateInstanceByName<ISampleController>(selectedSampleName);
DrawSampleName(selectedSampleName);
sampleController.Initialize();
Console.WriteLine("Starting...");
var selection = sampleController.CreateSelection();
var crossover = sampleController.CreateCrossover();
var mutation = sampleController.CreateMutation();
var fitness = sampleController.CreateFitness();
var population = new Population(100, 200, sampleController.CreateChromosome());
population.GenerationStrategy = new PerformanceGenerationStrategy();
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = sampleController.CreateTermination();
var terminationName = ga.Termination.GetType().Name;
ga.GenerationRan += delegate
{
DrawSampleName(selectedSampleName);
var bestChromosome = ga.Population.BestChromosome;
Console.WriteLine("Termination: {0}", terminationName);
Console.WriteLine("Generations: {0}", ga.Population.GenerationsNumber);
Console.WriteLine("Fitness: {0,10}", bestChromosome.Fitness);
Console.WriteLine("Time: {0}", ga.TimeEvolving);
sampleController.Draw(bestChromosome);
};
try
{
sampleController.ConfigGA(ga);
ga.Start();
}
catch (Exception ex)
{
Console.ForegroundColor = ConsoleColor.DarkRed;
Console.WriteLine();
Console.WriteLine("Error: {0}", ex.Message);
Console.ResetColor();
Console.ReadKey();
return;
}
Console.ForegroundColor = ConsoleColor.DarkGreen;
Console.WriteLine();
Console.WriteLine("Evolved.");
Console.ResetColor();
Console.ReadKey();
Run();
}
