///<summary>
/// Breed the next generation of networks from the current best and overall best.
///</summary>
protected virtual void BreedNextGeneration()
{
int k = 0;
int l = 0;
for (int i = 0; i < population; i++)
{
if (i < population * breedingRepartitionCoef) // all time generation
{
hostPopulation[i].GetComponent <Controller>().NeuralNet = Evolution.CreateMutatedOffspring(
alltimeFittestNets[k], l + 1,
hiddenLayerNbMutation, hiddenLayerNbMutationRate,
hiddenNbMutation, hiddenLayerNbMutationRate,
synapsesMutationRate, synapsesMutationRange); // l+1 as mutate coef to make each version more propable to mutate a lot
l += 1;
if (l > population * breedingRepartitionCoef / breedingSampleNb)
{
l = 0;
k += 1;
}
}
else if (i < population * (1 - freshBloodProportion)) // this generation
{
hostPopulation[i].GetComponent <Controller>().NeuralNet = Evolution.CreateMutatedOffspring(
generationFittestNets[k], l + 1,
hiddenLayerNbMutation, hiddenLayerNbMutationRate,
hiddenNbMutation, hiddenLayerNbMutationRate,
synapsesMutationRate, synapsesMutationRange);
l += 1;
if (l > population * (1 - freshBloodProportion) / breedingSampleNb)
{
l = 0;
k += 1;
}
}
else // fresh blood (10%)
{
hostPopulation[i].GetComponent <Controller>().NeuralNet = new Network(baseLayersSizes);
}
if (k >= breedingSampleNb)
{
k = 0;
}
}
}
/// <summary>
/// Sorts the current generation of networks by fitness.
/// </summary>
protected virtual void SortCurrentGeneration()
{
if (generationFittestNets[generationFittestNets.Length - 1] != null)
{
for (int i = 0; i < generationFittestNets.Length; i++)
{
generationFittestNets[i].FitnessScore = 0;
}
}
for (int i = 0; i < hostPopulation.Length; i++)
{
layered.Network fitnessContender = hostPopulation[i].GetComponent <Controller>().NeuralNet;
Evolution.RankFitnessContender(alltimeFittestNets, fitnessContender.GetClone());
Evolution.RankFitnessContender(generationFittestNets, fitnessContender.GetClone());
}
}
