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;
}
private static int RouletteSelection(Population pPreviousPopulation)
{
int min = 0;
int max = pPreviousPopulation.GetGenomes().Count - 1;
return GA.mRandom.Next(min, max);
}
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();
}
