public void Epoch()
{
CalcFitness();
var newpopulations = new List <IGenome>();
Population = Population.OrderBy(o => o.Fitness).ToList();
for (int j = 0; j < GASettings.BestSolutionToPick; j++)
{
Population[j].Fitness = Fitness.Calc(Population[j]);
Population[j].Score = 0;
newpopulations.Add(Population[j]);
}
int ran = Rand.Next(1, Population.Count);
while (newpopulations.Count < Population.Count)
{
// Selection
var nodemom = Selection.Select(Population);
var nodedad = Selection.Select(Population);
// CrossOver
var cross = Crossover.Calc(new CrossoverOperation(nodemom, nodedad));
//// Mutation
nodemom = Mutate.Calc(cross.Mom);
nodedad = Mutate.Calc(cross.Dad);
// Adaptation
nodemom = Adaptation.Calc(nodemom);
nodedad = Adaptation.Calc(nodedad);
nodemom.Fitness = Fitness.Calc(nodemom);
nodedad.Fitness = Fitness.Calc(nodedad);
// Add in new population
newpopulations.Add(nodemom);
newpopulations.Add(nodedad);
}
Population = null;
Population = newpopulations.ToList();
Generation++;
}
//// most time consuming, can be run in parallel
//private float[] calculatePopulationFitness()
//{
// for (int i = 0; i < fitnessParams.Length; i++)
// fs[i] = Fitness.fitness(researchData, fitnessParams[i]);
// return fs;
//}
// distribute processing on threads
public void calculatePopulationFitnessParallel()
{
int nExperiments = population.Length;
_numerOfThreadsNotYetCompleted = nExperiments;
_doneEvent = new ManualResetEvent(false);
fitnessObjects = new Fitness[nExperiments];
fitnesses = new float[nExperiments];
for (int i = 0; i < nExperiments; i++)
{
fitnessObjects[i] = new Fitness(researchDatas[i], fitnessParams[i]);
ThreadPool.QueueUserWorkItem(ThreadPoolCallback, i);
}
// Wait for all threads in pool to calculate.
_doneEvent.WaitOne();
Console.WriteLine("All calculations are complete.");
}
public int CompareTo(Chromosome other) => Fitness.CompareTo(other.Fitness);
public GAMachine(Population population, ISelection selection, ICrossover crossover, IMutation mutation, Fitness fitness, StopCondition stopCondition, double crossoverProbability = DefaultCrossoverProbability, double mutationProbability = DefaultMutationProbability)
{
_population = population;
_selection = selection;
_crossover = crossover;
_mutation = mutation;
_adjustment = new BestReservationAdjustment();
_fitness = fitness;
_stopCondition = stopCondition;
_crossoverProbability = crossoverProbability;
_mutationProbability = mutationProbability;
_population.ReInit();
}
