public override void Evolve()
{
int individualsToEliminate = (int)(_generationGap * _populationSize);
// A single generation is when GenerationGap % of population is eliminated and replaced
IList <T> offspringPopulation = new List <T>();
while (offspringPopulation.Count < individualsToEliminate)
{
IList <T> matingPool = new List <T>(CrossoverOperator.NumberOfParents);
for (int j = 0; j < CrossoverOperator.NumberOfParents; j++)
{
T mate = SelectionOperator.Execute(_population);
matingPool.Add(mate);
}
IList <T> children = CrossoverOperator.Execute(matingPool);
for (int j = 0; j < children.Count; j++)
{
MutationOperator.Execute(children[j]);
_problem.Evaluate(children[j]);
offspringPopulation.Add(children[j]);
}
}
offspringPopulation = offspringPopulation.Take(individualsToEliminate).ToList();
_population = ReplacementOperator.Execute(_population, offspringPopulation);
UpdateState();
}
public override void Evolve()
{
IList <T> offspringPopulation = new List <T>();
// Let elites survive to next generation
int numberOfElites = (int)(_elitismRate * _populationSize);
IList <T> elites = _population
.OrderByDescending(c => c, _problem.FitnessComparer)
.Take(numberOfElites).ToList();
foreach (T individual in elites)
{
offspringPopulation.Add(individual);
}
while (offspringPopulation.Count < _populationSize)
{
IList <T> matingPool = new List <T>(CrossoverOperator.NumberOfParents);
for (int i = 0; i < CrossoverOperator.NumberOfParents; i++)
{
matingPool.Add(SelectionOperator.Execute(_population));
}
IList <T> children = CrossoverOperator.Execute(matingPool);
for (int i = 0; i < children.Count; i++)
{
MutationOperator.Execute(children[i]);
_problem.Evaluate(children[i]);
offspringPopulation.Add(children[i]);
}
}
// When there is some excess chromosomes, discard them by taking only the needed ones
_population = offspringPopulation.Take(_populationSize).ToList();
UpdateState();
}
