/// <summary>
/// Создаем геномы и заполняем их генами
/// </summary>
private void CreateGenomes()
{
for (var i = 0; i < PopulationSize; i++)
{
Genome genome = new Genome();
genome.CreateGenes();
ThisGeneration.Add(genome);
}
}
/// <summary>
/// Создание следующего поколения
/// </summary>
private void CreateNextGeneration()
{
NextGeneration.Clear();
Genome genome = null;
if (Elitism)
{
genome = ThisGeneration[PopulationSize - 1];
}
for (var p = 0; p < PopulationSize; p += 2)
{
int parentIndexFirst = RouletteSelection();
int parentIndexSecond = RouletteSelection();
Genome parentFirst, parentSecond, childFirst, childSecond;
parentFirst = ThisGeneration[parentIndexFirst];
parentSecond = ThisGeneration[parentIndexSecond];
if (Rand.NextDouble() < CrossoverRate)
{
parentFirst.Crossover(ref parentSecond, out childFirst, out childSecond);
}
else
{
childFirst = parentFirst;
childSecond = parentSecond;
}
childFirst.Mutate();
childSecond.Mutate();
NextGeneration.Add(childFirst);
NextGeneration.Add(childSecond);
}
if (Elitism && genome != null)
{
NextGeneration[0] = genome;
}
ThisGeneration.Clear();
for (var p = 0; p < PopulationSize; p++)
{
ThisGeneration.Add(NextGeneration[p]);
}
}
/// <summary>
/// Сортируем популяции и сортируем в порядке пригодности
/// </summary>
private void RankPopulation()
{
TotalFitness = 0;
for (var p = 0; p < PopulationSize; p++)
{
Genome genome = ThisGeneration[p];
genome.Fitness = FitnessFunction(genome.Genes);
TotalFitness += genome.Fitness;
}
ThisGeneration.Sort(new GenomeComparison());
double fitness = 0.0;
FitnessTable.Clear();
for (var p = 0; p < PopulationSize; p++)
{
fitness += (ThisGeneration[p]).Fitness;
FitnessTable.Add(fitness);
}
}
