private void Crossover(NNet[] newPopulation)
{
for (int i = 0; i < numberToCrossover; i += 2)
{
int AIndex = i;
int BIndex = i + 1;
if (genePool.Count >= 1)
{
for (int l = 0; l < 100; l++)
{
AIndex = genePool[Random.Range(0, genePool.Count)];
BIndex = genePool[Random.Range(0, genePool.Count)];
if (AIndex != BIndex)
{
break;
}
}
}
NNet Child1 = new NNet();
NNet Child2 = new NNet();
Child1.Initialise(controller.LAYERS, controller.NEURONS);
Child2.Initialise(controller.LAYERS, controller.NEURONS);
Child1.fitness = 0;
Child2.fitness = 0;
for (int w = 0; w < Child1.weights.Count; w++)
{
if (Random.Range(0.0f, 1.0f) < 0.5f)
{
Child1.weights[w] = population[AIndex].weights[w];
Child2.weights[w] = population[BIndex].weights[w];
}
else
{
Child2.weights[w] = population[AIndex].weights[w];
Child1.weights[w] = population[BIndex].weights[w];
}
}
for (int w = 0; w < Child1.biases.Count; w++)
{
if (Random.Range(0.0f, 1.0f) < 0.5f)
{
Child1.biases[w] = population[AIndex].biases[w];
Child2.biases[w] = population[BIndex].biases[w];
}
else
{
Child2.biases[w] = population[AIndex].biases[w];
Child1.biases[w] = population[BIndex].biases[w];
}
}
newPopulation[naturallySelected] = Child1;
naturallySelected++;
newPopulation[naturallySelected] = Child2;
naturallySelected++;
}
}
public Genome(int nInput, int nHiddenLayer, int nHiddenLayerNeurons, int nOutput, int genomeID)
{
net = new NNet();
net.CreateNet(nInput, nHiddenLayer, nHiddenLayerNeurons, nOutput);
this.ID = genomeID;
}
public Genome(Genome other, int ID)
{
this.fitness = other.fitness;
this.ID = ID;
this.net = other.net;
}
public void SetPlayer(PlayerController followingPlayer)
{
ResetStructure();
nnet = followingPlayer.brain.nnet;
CreateStructure();
}
private void Crossover(NNet[] newPopulation)
{
for (int i = 0; i < Hijos; i += 2)
{
int AIndex = i;
int BIndex = i + 1;
if (genePool.Count >= 1)
{
for (int j = 0; j < 100; j++)
{
AIndex = genePool[Random.Range(0, genePool.Count)];
BIndex = genePool[Random.Range(0, genePool.Count)];
if (AIndex != BIndex)
{
break;
}
}
}
NNet child1 = new NNet();
NNet child2 = new NNet();
child1.Initialise(controller.LAYERS, controller.NEURONS);
child2.Initialise(controller.LAYERS, controller.NEURONS);
child1.fitness = 0;
child2.fitness = 0;
for (int k = 0; k < child1.weights.Count; k++)
{
if (Random.Range(0.0f, 1.0f) < 0.5f)
{
child1.weights[k] = population[AIndex].weights[k];
child2.weights[k] = population[BIndex].weights[k];
}
else
{
child1.weights[k] = population[BIndex].weights[k];
child2.weights[k] = population[AIndex].weights[k];
}
}
for (int k = 0; k < child1.biases.Count; k++)
{
if (Random.Range(0.0f, 1.0f) < 0.5f)
{
child1.biases[k] = population[AIndex].biases[k];
child2.biases[k] = population[BIndex].biases[k];
}
else
{
child1.biases[k] = population[BIndex].biases[k];
child2.biases[k] = population[AIndex].biases[k];
}
}
newPopulation[naturallySelected] = child1;
naturallySelected++;
newPopulation[naturallySelected] = child2;
naturallySelected++;
}
}
public void ResetWithNetwork(NNet net)
{
network = net;
Reset();
}
private void Awake()
{
startPosition = transform.position;
startRotation = transform.eulerAngles;
network = GetComponent <NNet>();
}
public void setNetwork(NNet net)
{
brain = net;
}
