/// <summary>
///
/// </summary>
public void CreateNewGeneration()
{
Generations[NbGeneration - 1] = Generations[NbGeneration - 1].OrderByDescending(x => x.Fitness).ToArray();
//Checker si shallow copy
NetworkGenome[] generationTemp = new NetworkGenome[GenerationSize];
NetworkGenome[] elites = GetElites(NbGeneration);
float totalFitness = GetTotalFitness(NbGeneration);
int indexNewGen = 0;
int indexPartner = -1;
//garde lelite
for (int i = 0; i < elites.Length && indexNewGen < GenerationSize; i++)
{
generationTemp[indexNewGen++] = elites[i];
}
//rajoute random pour diversiter
for (int i = 0; i < (int)(LoserPercent * GenerationSize) && indexNewGen < GenerationSize; i++)
{
generationTemp[indexNewGen++] = new NetworkGenome(NeuralNetwork.Layout, NeuralNetwork.NbNecessaryInputs, NeuralNetwork.NetworkSize, MutationRnd, CrossOverRnd, MutationChance);
}
//fill avec des enfants
while (indexNewGen < GenerationSize)
{
indexPartner = ChooseMateIndex(NbGeneration);
Genome[] enfants = Generations[NbGeneration - 1][indexPartner].ProduceOffspring(Generations[NbGeneration - 1][ChooseMateIndex(NbGeneration, indexPartner)]);
generationTemp[indexNewGen++] = (enfants[0] as NetworkGenome);
generationTemp[indexNewGen++] = (enfants[1] as NetworkGenome);
}
Generations.Add(generationTemp);
}
public NetworkGenome(NetworkGenome genom) :
base(genom.Dna, genom.MutationRnd, genom.CrossOverRnd)
{
Equation = genom.Equation;
Layout = genom.Layout;
NbInputs = genom.NbInputs;
Fitness = genom.Fitness;
}
/// <summary>
///
/// </summary>
public void CreateFirstGeneration()
{
NetworkGenome[] generationTemp = new NetworkGenome[GenerationSize];
for (int i = 0; i < GenerationSize; i++)
{
generationTemp[i] = new NetworkGenome(NeuralNetwork.Layout, NeuralNetwork.NbNecessaryInputs,
NeuralNetwork.NetworkSize, MutationRnd, CrossOverRnd, MutationChance, NeuralNetwork.Layers[0].Neurons[0].MinValueRand, NeuralNetwork.Layers[0].Neurons[0].MaxValueRand);
}
Generations.Add(generationTemp);
}
/// <summary>
///
/// </summary>
/// <param name="partner"></param>
/// <returns></returns>
public override Genome[] ProduceOffspring(Genome partner)
{
Genome[] r = new Genome[2];
if (Dna.Length != partner.Dna.Length)
{
//if (partner is NetworkGenome)
// return ProduceOffspringNeat(partner as NetworkGenome);
//else
// throw new Exception("Les deux chromosones ne sont pas compatibles");
}
NetworkGenome Children1 = new NetworkGenome(this);
NetworkGenome Children2 = new NetworkGenome(this);
int indexDna = 0;
int crossPoint = 0;;
for (int layer = 0; layer < this.Layout.Length; layer++)
{
for (int node = 0; node < this.Layout[layer]; node++)
{
//pour skip les biais A CHECKER
indexDna++;
crossPoint = CrossOverRnd.Next(0, ((layer == 0) ? NbInputs : Layout[layer - 1]));
for (int weigths = 0; weigths < ((layer == 0) ? NbInputs:Layout[layer - 1]); weigths++)
{
if (weigths < crossPoint)
{
Children1.Dna[indexDna] = partner.Dna[indexDna];
}
else
{
Children2.Dna[indexDna] = partner.Dna[indexDna];
}
indexDna++;
}
}
}
Children1.Mutate();
Children2.Mutate();
Children1.Fitness = -1;
Children2.Fitness = -1;
r[0] = Children1;
r[1] = Children2;
return(r);
}
public Tuple <Genome, Genome> ProduceOffspringNeat(NetworkGenome partner)
{
throw new NotImplementedException();
}
