public void NewGeneration(int numNewDNA = 0, bool crossoverNewDNA = false)
{
int finalCount = popsize + numNewDNA;
if (finalCount <= 0)
{
return;
}
if (popsize > 0)
{
CalculateFitness();
Population.Sort(CompareDNA);
}
newPopulation.Clear();
for (int i = 0; i < popsize; i++)
{
if (i < Elitism && i < popsize)
{
newPopulation.Add(Population[i]);
}
else if (crossoverNewDNA)
{
GenerateQP <T> parent1 = ChooseParent();
GenerateQP <T> parent2 = ChooseParent();
//if (parent1 == null)
//{
// return;
//}
GenerateQP <T> child = parent1.Crossover(parent2);
// if (child == null)
//{
// return;
//}
//child.Mutate(MutationRate);
newPopulation.Add(child);
}
else
{
GenerateQP <T> generateclone = new GenerateQP <T>(dnaSize, random, getRandomGene, fitnessFunction, shouldInitGenes: true);
newPopulation.Add((GenerateQP <T>)generateclone.Clone());
}
}
List <GenerateQP <T> > tmpList = new List <GenerateQP <T> >(Population);
Population = new List <GenerateQP <T> >(newPopulation);
newPopulation = new List <GenerateQP <T> >(tmpList);
Generation++;
}
public GeneticAlgorithm(int populationSize, int dnaSize, Random random, Func <int, T> getRandomGene, Func <GenerateQP <T>, float> fitnessFunction,
int elitism, float mutationRate = 0.01f)
{
popsize = populationSize;
Generation = 1;
Elitism = elitism;
MutationRate = mutationRate;
Population = new List <GenerateQP <T> >();
newPopulation = new List <GenerateQP <T> >();
this.random = random;
this.dnaSize = dnaSize;
this.getRandomGene = getRandomGene;
this.fitnessFunction = fitnessFunction;
BestGenes = new T[dnaSize];
for (int i = 0; i < popsize; i++)
{
GenerateQP <T> generateclone = new GenerateQP <T>(dnaSize, random, getRandomGene, fitnessFunction, shouldInitGenes: true);
Population.Add((GenerateQP <T>)generateclone.Clone());
}
CalculateFitness();
}