public Individual <T>[][] Apply(Individual <T>[] population, int parentGroupsCount, int parentsPerGroup)
{
double[] expectations = fitnessScaling.CalculateExpectations(population);
Roulette roulette = Roulette.CreateFromPositive(expectations, rng);
var parentGroups = new Individual <T> [parentGroupsCount][];
for (int group = 0; group < parentGroupsCount; ++group)
{
parentGroups[group] = new Individual <T> [parentsPerGroup];
for (int parent = 0; parent < parentsPerGroup; ++parent)
{
Individual <T> individual = population[roulette.SpinWheelWithBall()];
if (!allowIdenticalParents)
{
while (parentGroups[group].Contains <Individual <T> >(individual))
{
individual = population[roulette.SpinWheelWithBall()];
}
}
parentGroups[group][parent] = individual;
}
}
return(parentGroups);
}
protected override sealed Individual <T>[] RemainderPhase(Individual <T>[] population, double[] fractionalParts,
int extrasParentsCount)
{
Roulette roulette = Roulette.CreateFromPositive(fractionalParts, RNG);
var extraParents = new Individual <T> [extrasParentsCount];
for (int i = 0; i < extrasParentsCount; ++i)
{
extraParents[i] = population[roulette.SpinWheelWithBall()];
}
return(extraParents);
}
private Individual <T>[] SampleAllAtOnce(Individual <T>[] population, int totalParentsCount)
{
double[] expectations = fitnessScaling.CalculateExpectations(population);
Roulette roulette = Roulette.CreateFromPositive(expectations, rng);
int[] selectedIndexes = roulette.SpinWheelWithPointers(totalParentsCount);
var allParents = new Individual <T> [totalParentsCount];
for (int i = 0; i < totalParentsCount; ++i)
{
allParents[i] = population[selectedIndexes[i]];
}
return(allParents);
}