public GeneticAlgorithm(int selectedFitnessFunction, int populationSize, int iterationsNumber, int selectedMethod)
{
this.selectedMethod = selectedMethod;
ISelectionMethod selectionMethot = GetSelectionMethod();
DoubleArrayChromosome chromosome = new DoubleArrayChromosome(new AForge.Math.Random.StandardGenerator(), new AForge.Math.Random.StandardGenerator(), new AForge.Math.Random.StandardGenerator(), 2);
FitnessFunction fitnessfunction = new FitnessFunction(selectedFitnessFunction);
Population population = new Population(populationSize, chromosome, fitnessfunction, selectionMethot);
double[,] tmp = new double[iterationsNumber, 2];
this.result = new double[iterationsNumber, 2];
int i;
for (i = 0; i < iterationsNumber; i++)
{
population.RunEpoch();
DoubleArrayChromosome bestChromosome = (DoubleArrayChromosome)population.BestChromosome;
double x = (double)bestChromosome.Value.GetValue(0);
double y = (double)bestChromosome.Value.GetValue(1);
tmp[i, 0] = x;
tmp[i, 1] = y;
if (fitnessfunction.Evaluate(bestChromosome) == 100)
{
break;
}
}
this.result = new double[i, 2];
for (int j = 0; j < i; j++)
{
result[j, 0] = tmp[j, 0];
result[j, 1] = tmp[j, 1];
}
}
public void Evaluate()
{
Parallel.ForEach(GenePool, genotype =>
{
genotype.Fitness = FitnessFunction.Evaluate(genotype);
});
}
private void UpdateFitnesses()
{
foreach (Program p in m_Progs)
{
if (p.FitnessIsDirty)
{
p.Fitness = FitnessFunction.Evaluate(p);
}
}
}
/// <summary>
/// Evaluates the fitness of all individuals in the population
/// </summary>
/// <param name="pop">the population</param>
/// <param name="count">propagating information where spanners are counted</param>
public void Evaluate(Population pop, bool count)
{
double average = 0;
pop.GetIndividuals().ForEach(x =>
{
double fit = fitness.Evaluate(x, count);
x.SetFitnessValue(fit);
average += fit;
});
// set average of population for console output
pop.SetAverage(average / pop.GetPopulationSize());
}