private void choose_crossover_method(bool isOrdered = true)
{
Console.WriteLine("Please Choose CrossOver Method :");
var methodsList = Enum.GetValues(typeof(CrossoverMethod)).Cast <CrossoverMethod>().ToList();
if (isOrdered == true)
{
for (int i = 0; i < 3; i++)
{
methodsList.RemoveAt(0);
}
}
else
{
for (int i = 0; i < 4; i++)
{
methodsList.Remove(methodsList.Last());
}
}
for (int i = 0; i < methodsList.Count; i++)
{
Console.WriteLine((i + 1) + ". " + methodsList[i]);
}
int input = 0;
do
{
input = get_input();
} while (input <= 0 || input > methodsList.Count);
_crossoverMethod = methodsList[input - 1];
}
//Returns a list of new individuals bred from the current population
private List <AIPlayer> BreedIndividuals()
{
int crossovers = (int)(Simulation.PopulationSize * Simulation.CrossoverBredAmount);
int mutations = Simulation.PopulationSize - crossovers;
int crossoverMutations = (int)(Simulation.MutateAfterCrossoverAmount * Simulation.MutateAfterCrossoverAmount);
crossovers -= crossoverMutations;
List <AIPlayer> newlyBred = new List <AIPlayer>();
for (int i = 0; i < mutations; i++)
{
AIPlayer parent = SelectIndividualRankBased();
Chromosome newChromosome = parent.Chromosome.GetMutated(Simulation.MutationRate);
AIPlayer toAdd = new AIPlayer(newChromosome, parent, null, Simulation.NeuralNetworkMaker);
newlyBred.Add(toAdd);
}
for (int i = 0; i < crossovers; i++)
{
AIPlayer parent1 = SelectIndividualRankBased();
AIPlayer parent2 = SelectIndividualRankBased();
CrossoverMethod crossoverMethod = Simulation.RandomCrossoverMethod();
Chromosome crossedChromosome = crossoverMethod.Cross(parent1.Chromosome, parent2.Chromosome);
AIPlayer toAdd = new AIPlayer(crossedChromosome, parent1, parent2, Simulation.NeuralNetworkMaker);
newlyBred.Add(toAdd);
}
for (int i = 0; i < crossoverMutations; i++)
{
AIPlayer parent1 = SelectIndividualRankBased();
AIPlayer parent2 = SelectIndividualRankBased();
CrossoverMethod crossoverMethod = Simulation.RandomCrossoverMethod();
Chromosome crossedChromosome = crossoverMethod.Cross(parent1.Chromosome, parent2.Chromosome);
Chromosome crossedAndMutatedChromosome = crossedChromosome.GetMutated(Simulation.MutationRate);
AIPlayer toAdd = new AIPlayer(crossedAndMutatedChromosome, parent1, parent2, Simulation.NeuralNetworkMaker);
newlyBred.Add(toAdd);
}
//Parallel.For(0, newlyBred.Count, i => newlyBred[i].CalcFitness(Simulation.Game));
foreach (AIPlayer aip in newlyBred)
{
aip.CalcFitness(Simulation.Game);
}
return(newlyBred);
}
