public static List <Genome> GenerateGenomes(int m_populationSize, Population pPreviousPopulation)
{
List <Genome> m_nextGeneration = new List <Genome>();
for (int i = 0; i < m_populationSize; i += 2)
{
int pidx1 = RouletteSelection(pPreviousPopulation);
int pidx2 = RouletteSelection(pPreviousPopulation);
Genome parent1;
Genome parent2;
Genome child1;
Genome child2;
parent1 = pPreviousPopulation[pidx1];
parent2 = pPreviousPopulation[pidx2];
if (GA.mRandom.NextDouble() < CrossoverRate)
{
GA.Crossover(parent1, parent2, out child1, out child2);
}
else
{
child1 = new Genome(parent1);
child2 = new Genome(parent2);
}
Mutator(child1);
Mutator(child2);
m_nextGeneration.Add(new Genome(child1));
m_nextGeneration.Add(new Genome(child2));
}
return(m_nextGeneration);
}
public static void Mutator(Genome pGenome)
{
for (int pos = 0; pos < Genome.mLength; pos++)
{
if (GA.mRandom.NextDouble() < MutationRate)
{
pGenome[pos] = GA.GetRandomGene();
}
}
}
public static void Main(string[] args)
{
List <Population> mPopulations = new List <Population>();
GA.CrossoverRate = 0.5;
GA.MutationRate = 0.1;
Genome.mLength = 10;
int m_populationSize = 10;
double m_selectionRate = 0.8; //80% kills in population
int m_generationSize = 2000;
Population pop = new Population(0, GA.GenerateInitialGenomes(m_populationSize));
pop.DoMagic(m_selectionRate);
pop.Info();
mPopulations.Add(pop);
for (int i = 0; i < m_generationSize; i++)
{
Population pop2 = new Population(i, GA.GenerateGenomes(m_populationSize, mPopulations[i]));
pop2.DoMagic(m_selectionRate);
pop2.Info();
mPopulations.Add(pop2);
if (pop2.mMaxFitness > 9.8)
{
break;
}
}
mPopulations.Sort(delegate(Population x, Population y) {
if (x.GetFitness() > y.GetFitness())
{
return(1);
}
else if (x.GetFitness() == y.GetFitness())
{
return(0);
}
else
{
return(-1);
}
});
mPopulations[mPopulations.Count - 1].Output();
}
