/// <summary>
/// Realizuje algorytm selekcji.
/// </summary>
/// <param name="population">Populacja poddawana selekcji.</param>
/// <returns>Zbiór osobników populacji, wybranych w wyniku selekcji do następnej generacji.</returns>
public IChromosome[] Select(IChromosome[] population)
{
if (population.Length < 2)
{
return population;
}
IChromosome[] subpopulation = population.Where(ch => ch.Evaluate() > 0).ToArray();
Double totalFitness = subpopulation.Sum(ch => ch.Evaluate());
Int32 newPopulationSize = (Int32)PopulationSize.ComputeSize(subpopulation);
IChromosome[] result = new IChromosome[newPopulationSize];
for (Int32 i = 0; i < newPopulationSize; i++)
{
Double ptr = RandomGenerator.NextDouble();
Double sum = 0.0;
for (Int32 j = 0; j < subpopulation.Length; ++j)
{
sum += subpopulation[j].Evaluate() / totalFitness;
if (sum > ptr)
{
result[i] = subpopulation[j];
break;
}
}
}
return result;
}
/// <summary>
/// Realizuje algorytm selekcji.
/// </summary>
/// <param name="population">Populacja poddawana selekcji.</param>
/// <returns>Zbiór osobników populacji, wybranych w wyniku selekcji do następnej generacji.</returns>
public IChromosome[] Select(IChromosome[] population)
{
if (population.Length < 2)
{
return population;
}
IChromosome[] subpopulation = population.Where(ch => ch.Evaluate() > 0).ToArray();
Double totalFitness = subpopulation.Sum(ch => ch.Evaluate());
Int32 newPopulationSize = (Int32)PopulationSize.ComputeSize(subpopulation);
IChromosome[] result = new IChromosome[newPopulationSize];
Double ptrstep = 1.0 / newPopulationSize;
Double ptr = RandomGenerator.NextDouble() * ptrstep;
Int32 pos = 0;
Double sum = 0.0;
for (Int32 i = 0; i < subpopulation.Length; i++)
{
for (sum += subpopulation[i].Evaluate() / totalFitness; sum > ptr; ptr += ptrstep)
{
result[pos++] = subpopulation[i];
if (pos == newPopulationSize)
{
return result;
}
}
}
// it shouldn't happen
Debug.Assert(false);
return result;
}
