static void Main(string[] args)
{
//
GraphModel graphModel = new GraphModel("data/data_35ver.csv");
int vezirSayisi = graphModel.vertexes.Count;
var selection = new EliteSelection();
var crossover = new TwoPointCrossover();
var mutation = new ReverseSequenceMutation();
var fitness = new MyProblemFitness(graphModel);
var chromosome = new MyProblemChromosome(vezirSayisi);
var population = new Population(5000, 5500, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
//Termination = new FitnessThresholdTermination(vezirSayisi * (vezirSayisi - 1) / 2)
Termination = new FitnessThresholdTermination(100)
};
int index = 0;
ga.GenerationRan += delegate {
var bestChromosome = ga.Population.BestChromosome;
Console.Write("Index: " + index);
Console.Write(", Fitness: {0}", bestChromosome.Fitness);
Console.Write(", Genes: {0}", string.Join("-", bestChromosome.GetGenes()));
bestChromosome.GetGenes();
Console.Write($", Ilość kolorow: {MyProblemFitness.GetCountOfColors(bestChromosome.GetGenes())}");
Console.WriteLine();
index++;
};
Console.WriteLine("GA running...");
ga.Start();
Console.WriteLine("Best solution found has {0} fitness.", ga.BestChromosome.Fitness);
Console.Read();
}
static void Main(string[] args)
{
int vezirSayisi = 8;
var selection = new EliteSelection();
var crossover = new TwoPointCrossover();
var mutation = new ReverseSequenceMutation();
var fitness = new MyProblemFitness();
var chromosome = new MyProblemChromosome(vezirSayisi);
var population = new Population(5000, 5500, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new FitnessThresholdTermination(vezirSayisi * (vezirSayisi - 1) / 2)
};
int index = 0;
ga.GenerationRan += delegate
{
var bestChromosome = ga.Population.BestChromosome;
Console.Write("Index: " + index);
Console.Write(", Fitness: {0}", bestChromosome.Fitness);
Console.Write(", Genes: {0}", string.Join("-", bestChromosome.GetGenes()));
Console.WriteLine();
index++;
};
Console.WriteLine("GA running...");
ga.Start();
Console.WriteLine("Best solution found has {0} fitness.", ga.BestChromosome.Fitness);
Console.Read();
}