public void NewGeneration(int numNewDNA = 0, bool crossoverNewDNA = false)
{
int finalCount = Population.Count + numNewDNA;
if (finalCount <= 0)
{
return;
}
if (Population.Count > 0)
{
CalculateFitness();
Population.Sort(CompareDNA);
}
newPopulation.Clear();
for (int i = 0; i < Population.Count; i++)
{
if (i < Elitism && i < Population.Count)
{
newPopulation.Add(Population[i]);
}
else if (i < Population.Count || crossoverNewDNA)
{
DNA <T> parent1 = ChooseParent();
DNA <T> parent2 = ChooseParent();
DNA <T> child = parent1.Crossover(parent2);
child.Mutate(MutationRate);
newPopulation.Add(child);
}
else
{
newPopulation.Add(new DNA <T>(dnaSize, random, getRandomGene, fitnessFunction, shouldInitGenes: true));
}
}
List <DNA <T> > tmpList = Population;
Population = newPopulation;
newPopulation = tmpList;
Generation++;
}
public void NewGeneration()
{
if (Population.Count <= 0)
{
return;
}
CalculateFitness();
Population.Sort(CompareDNA);
newPopulation.Clear();
for (int i = 0; i < Population.Count; i++)
{
// we keep the 5 best individual (the list is sorted)
if (i < Elitism)
{
newPopulation.Add(Population[i]);
}
else
{
DNA <T> parent1 = ChooseParent();
DNA <T> parent2 = ChooseParent();
DNA <T> child = parent1.Crossover(parent2);
child.Mutate(MutationRate);
newPopulation.Add(child);
}
}
//Memory optimization (we dont have to always create a new list)
List <DNA <T> > tmpList = Population;
Population = newPopulation;
newPopulation = tmpList;
Generation++;
}