public BuildGeneticAlgorithm(int countGenes, int minSizePopulation = 2, int maxSizePopulation = 100)
{
_chromosome = new BinaryChromosome(countGenes);
_population = new GeneticSharp.Domain.Populations.Population(minSizePopulation, maxSizePopulation, _chromosome);
}
public static void BuildTestGA()
{
//Create chromosome length = 20
BinaryChromosome chrom = new BinaryChromosome(20);
//Create population = [2,100]
var population = new Population(2, 100, chrom);
//Console.WriteLine(chrom.Length);
//Console.WriteLine(chrom.ToString());
//create Fitness Function (функция приспособления)
var fitness = new FuncFitness(
(c) =>
{
var fc = c as BinaryChromosome;
double result = 0.0;
foreach (Gene gene in fc.GetGenes())
{
result += Convert.ToDouble(gene.Value.ToString());
}
return(result);
}
);
//create selection
//var selection = new EliteSelection();
var selection = new TournamentSelection();
//var selection = new RouletteWheelSelection();
//var selection = new StochasticUniversalSamplingSelection();
//create crossover
var crossover = new UniformCrossover(0.5f);
//var crossover = new CutAndSpliceCrossover(); //только с EliteSelection()
//var crossover = new OnePointCrossover();
//var crossover = new TwoPointCrossover();
//var crossover = new CycleCrossover(); // new OrderBasedCrossover(); new OrderedCrossover(); new PositionBasedCrossover(); new PartiallyMappedCrossover(); //может использоваться только с упорядоченными хромосомами. Указанная хромосома имеет повторяющиеся гены
//var crossover = new ThreeParentCrossover(); //ОДНУ Итерацию выполняет
//create mutation
var mutation = new FlipBitMutation();
//var mutation = new UniformMutation(); //1 перегрузка принимает индексы генов для мутации, 2-я все гены мутируют
//var mutation = new TworsMutation(); //Слабая
//var mutation = new ReverseSequenceMutation(); //Слабая
//create termination (Количество итераций)
var termination = new GenerationNumberTermination(100);
//var termination = new FitnessStagnationTermination(50);
// var termination = new FitnessThresholdTermination(50); //Постоянно зацикливается
//TimeSpan time = new TimeSpan(0, 0, 10); //10 секунд
//var termination = new TimeEvolvingTermination(time);
//Сам генетический алгоритм
var ga = new GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation);
ga.Termination = termination;
Console.WriteLine("Generation: = distance");
var latestFitness = 0.0;
ga.GenerationRan += (sender, e) =>
{
var bestChromosome = ga.BestChromosome as BinaryChromosome;
var bestFitness = bestChromosome.Fitness.Value;
if (bestFitness != latestFitness)
{
latestFitness = bestFitness;
var phenotype = bestChromosome.GetGenes();
//Console.WriteLine(
// "Generation {0,2}: ({1},{2}),({3},{4}) = {5}",
// ga.GenerationsNumber,
// phenotype[0],
// phenotype[1],
// phenotype[2],
// phenotype[3],
// bestFitness
//);
Console.WriteLine("Generation {0,2}. Best Fitness = {1}", ga.GenerationsNumber, bestFitness);
Console.Write("Chromosome: ");
foreach (Gene g in phenotype)
{
Console.Write(g.Value.ToString() + "");
}
Console.WriteLine();
}
};
ga.Start();
}
