private void ApplyCrossover(
IOrderedEnumerable <Chromosome <T> > sortedPopulation,
List <Chromosome <T> > newPopulation)
{
while (newPopulation.Count() != geneticOptions.PopulationSize)
{
var parentA = geneticAlgorithm.ChooseParent(sortedPopulation);
var parentB = geneticAlgorithm.ChooseParent(sortedPopulation);
if (random.NextDouble() < geneticOptions.CrossoverProbability)
{
newPopulation.Add(geneticAlgorithm.Crossover(parentA, parentB));
}
}
}
public void NextGeneration()
{
FitnessStats.SaveCurrentToHistory();
Generation++;
var newGenomes = new List <Genome>();
if (_genetics.EliteCount > 0)
{
var elites = Genomes.OrderByDescending(x => x.Fitness).Take(_genetics.EliteCount);
newGenomes.AddRange(elites);
}
while (newGenomes.Count < Genomes.Count())
{
var mother = getGenomeByRoulette();
var father = getGenomeByRoulette();
var children = _genetics.Crossover(mother, father);
foreach (var child in children)
{
_genetics.Mutate(child);
newGenomes.Add(child);
}
}
newGenomes.ForEach(x => x.ResetFitness());
Genomes = newGenomes.Take(Genomes.Count()).ToList();
}