public static Chromosome Cross(Tuple<Chromosome, Chromosome> pair, Random random)
{
IList<double> values;
UniformDistribution uniform = new UniformDistribution(Interval.FromEndpoints(0, 1));
DiscreteUniformDistribution discrete = new DiscreteUniformDistribution(0, 1);
double randomNumber = uniform.GetRandomValue(random);
if (randomNumber <= SELECTION_1_PROBABILITY) // whole arithmetic recombination
values = pair.Item1.Values.Zip(pair.Item2.Values, (x, y) => (x + y) / 2).ToList();
else if (randomNumber <= SELECTION_1_PROBABILITY + SELECTION_2_PROBABILITY) // discrete recombination
values = pair.Item1.Values.Zip(pair.Item2.Values, (x, y) => discrete.GetRandomValue(random) == 0 ? x : y).ToList();
else // simple arithmetic recombination
values = pair.Item1.Values.Zip(pair.Item2.Values, (x, y) => pair.Item1.Values.IndexOf(x) < pair.Item1.Values.Count / 2 ? x : (x + y) / 2).ToList();
return new Chromosome(values);
}
public static Tuple<Chromosome, Chromosome> Select(IList<Chromosome> population, Random random, out Chromosome worst)
{
SortedSet<Chromosome> tournament = new SortedSet<Chromosome>();
DiscreteUniformDistribution discrete = new DiscreteUniformDistribution(0, population.Count - 1);
while (tournament.Count < K)
{
int index = discrete.GetRandomValue(random);
tournament.Add(population[index]);
}
worst = tournament.Max;
tournament.Remove(worst);
double sumFitness;
CalculateFitness(tournament, out sumFitness);
Chromosome parent1 = SelectParent(tournament, sumFitness, random);
tournament.Remove(parent1);
CalculateFitness(tournament, out sumFitness);
Chromosome parent2 = SelectParent(tournament, sumFitness, random);
return new Tuple<Chromosome, Chromosome>(parent1, parent2);
}
