private List <Individual> Krzyzuj(CrossMethod crossMethod, List <Individual> population, int a, int b)
{
var twoRandomIndividuals = GetTwoRandomIndividuals(population);
switch (crossMethod)
{
case CrossMethod.ONE_POINT:
return(_krzyzowanieJednopunktowe.Krzyzuj(twoRandomIndividuals[0], twoRandomIndividuals[1], a, b));
case CrossMethod.TWO_POINT:
return(_krzyzowanieDwupunktowe.Krzyzuj(twoRandomIndividuals[0], twoRandomIndividuals[1], a, b));
case CrossMethod.JEDNORODNE:
return(_krzyzowanieJednorodne.Krzyzuj(twoRandomIndividuals[0], twoRandomIndividuals[1], a, b));
default:
throw new NotImplementedException();
}
}
private ICross GetCrossMethod(CrossMethod crossMethod)
{
switch (crossMethod)
{
case (CrossMethod.OnePoint):
return(new OnePointCross());
case (CrossMethod.TwoPoints):
return(new TwoPointCross());
case (CrossMethod.ThreePoints):
return(new ThreePointCross());
case (CrossMethod.Homogeneous):
return(new HomogeneousCross());
default:
throw new ArgumentException("Cross method was not foud.");
}
}
public List <Individual> Execute(int a, int b, int populationAmount, int numberOfBits, int epochsAmount, double bestAndTournamentChomosomeAmount, double eliteStrategyAmount, double crossProbability, double mutationProbability, double inversionProbability, SelectionMethod selectionMethod, CrossMethod crossMethod, MutationMethod mutationMethod, bool maximization)
{
//Poczatkowa populacja
var population = _individualGenerator.GenerateList(populationAmount, numberOfBits, a, b);
DataSaver dataSaver = new DataSaver();
dataSaver.createFile();
var bestIndividuals = new List <Individual>();
for (int i = 0; i < epochsAmount; i++)
{
//Selekcja
var afterSelection = Wyselekcjuj(selectionMethod, population, bestAndTournamentChomosomeAmount, maximization);
var newPopulation = new List <Individual>();
// strategia elitarna - wez X procent najlepszych do nowej populacji
newPopulation.AddRange(_selekcjaNajlepszych.Select(afterSelection, eliteStrategyAmount, maximization));
//Mutacja, krzyzowanie i inwersja
while (newPopulation.Count < population.Count)
{
if (ShouldPerformAction(crossProbability) && newPopulation.Count + 1 < population.Count)
{
newPopulation.AddRange(Krzyzuj(crossMethod, afterSelection, a, b));
}
if (ShouldPerformAction(mutationProbability) && newPopulation.Count < population.Count)
{
newPopulation.Add(Mutuj(mutationMethod, afterSelection, a, b));
}
if (ShouldPerformAction(inversionProbability) && newPopulation.Count < population.Count)
{
newPopulation.Add(Inwersuj(afterSelection, a, b));
}
}
bestIndividuals.AddRange(_selekcjaNajlepszych.Select(newPopulation, 1.0 / (newPopulation.Count), maximization)); // dodanie najlepszego do listy
dataSaver.saveIndividualsFromEpoche(i, population);
population = newPopulation;
}
dataSaver.saveChart(bestIndividuals);
return(population);
}
