public static void ga()
{
AlgorithmList.loadformnistfsgm();
var selection = new EliteSelection();
var crossover = new OrderedCrossover();
var mutation = new ReverseSequenceMutation();
var fitness = new MyProblemFitness();
var chromosome = new MyChromosome();
var population = new Population(4, 6, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new GenerationNumberTermination(4);
Console.WriteLine("GA running...");
ga.Start();
Console.WriteLine("Best solution found has {0} fitness.", ga.BestChromosome.Fitness);
var a = ga.BestChromosome.GetGenes();
string s = "";
foreach (var b in a)
{
s = s + b.Value.ToString();
}
Console.WriteLine("Best solution found has {0} ", s);
}
public Fitnessvalues Evaluate(MyChromosome chromosome)
{
// fitnesstcheck(chromosome);
RunAlgorithm r = new RunAlgorithm();
return(r.RunAlgorithmchromosomes(chromosome));
}
public Fitnessvalues RunAlgorithmchromosomes(MyChromosome chromosome)
{
fitnesstcheck(chromosome);
imagetotal = Constant.samplesize;
threadcount = Constant.ThreadCount;
greyscaleenable = Constant.greyscale;
return(runonthread());
}
public CustomPopulation(int max, int min, int populationsize)
{
populationset.Clear();
populationset = new List <MyChromosome>();
Random random = new Random();
for (int i = 0; i < populationsize; i++)
{
MyChromosome chromosome = new MyChromosome(max, min, random);
// Console.WriteLine(chromosome.Gene);
populationset.Add(chromosome);
}
}
private void fitnesstcheck(MyChromosome chromosome)
{
checkstring(chromosome.GetGenes());
}
