private void Crossover(reinforcementNet[] newPopulation, reinforcement2Child[] newPopulation2)
{
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;
}
}
}
reinforcementNet Child1 = new reinforcementNet();
reinforcementNet Child2 = new reinforcementNet();
//
reinforcement2Child Child1_A = new reinforcement2Child();
reinforcement2Child Child2_A = new reinforcement2Child();
Child1.Initialise(controller.LAYERS, controller.NEURONS);
Child2.Initialise(controller.LAYERS, controller.NEURONS);
Child1_A.Initialise2(player2.LAYERS, player2.NEURONS);
Child2_A.Initialise2(player2.LAYERS, player2.NEURONS);
Child1.fitness = 0;
Child2.fitness = 0;
Child1_A.fitness = 0;
Child2_A.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];
//second person
Child2_A.weights[w] = population2[AIndex].weights[w];
Child1_A.weights[w] = population2[BIndex].weights[w];
}
else
{
Child2.weights[w] = population[AIndex].weights[w];
Child1.weights[w] = population[BIndex].weights[w];
//second person
Child1_A.weights[w] = population2[AIndex].weights[w];
Child2_A.weights[w] = population2[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];
//second person
Child2_A.biases[w] = population2[AIndex].biases[w];
Child1_A.biases[w] = population2[BIndex].biases[w];
}
else
{
Child2.biases[w] = population[AIndex].biases[w];
Child1.biases[w] = population[BIndex].biases[w];
//second person
Child1_A.biases[w] = population2[AIndex].biases[w];
Child2_A.biases[w] = population2[BIndex].biases[w];
}
}
newPopulation[naturallySelected] = Child1;
newPopulation2[naturallySelected] = Child1_A;
naturallySelected++;
newPopulation[naturallySelected] = Child2;
newPopulation2[naturallySelected] = Child2_A;
naturallySelected++;
}
}
private void Crossover(reinforcementNet[] newPopulation)
{
//cross over based on gene pool
//increment by two
for (int i = 0; i < numberToCrossover; i += 2)
{
//first parent index
int AIndex = i;
//second parent
int BIndex = i + 1;
if (genePool.Count >= 1)
{
for (int l = 0; l < 100; l++)
{
//find random elemnt in gene pool
AIndex = genePool[Random.Range(0, genePool.Count)];
BIndex = genePool[Random.Range(0, genePool.Count)];
//ensure parents are not the same
//we found what we looking for break
if (AIndex != BIndex)
{
break;
}
}
}
reinforcementNet Child1 = new reinforcementNet();
reinforcementNet Child2 = new reinforcementNet();
Child1.Initialise(controller.LAYERS, controller.NEURONS);
Child2.Initialise(controller.LAYERS, controller.NEURONS);
Child1.fitness = 0;
Child2.fitness = 0;
//should be more subtle in practice to much transfer of info
for (int w = 0; w < Child1.weights.Count; w++)
{
if (Random.Range(0.0f, 1.0f) < 0.5f)
{
//taje weights from parents
Child1.weights[w] = population[AIndex].weights[w];
Child2.weights[w] = population[BIndex].weights[w];
}
else
{
//random chance for kids
Child2.weights[w] = population[AIndex].weights[w];
Child1.weights[w] = population[BIndex].weights[w];
}
}
//indv values instead of matices
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];
}
}
//add to new population
newPopulation[naturallySelected] = Child1;
naturallySelected++;
//add to new population
newPopulation[naturallySelected] = Child2;
naturallySelected++;
}
}
