NeuralNetwork.Network CreateNewNetwork()
{
//Create a new
NeuralNetwork.Network newNetwork = Instantiate(neuralNetworkPrefab, transform);
newNetwork.inputLayer = input;
newNetwork.onOutput.AddListener(output.SetFly);
newNetwork.InitializeConnections();
//Add the new network to the list of networks
NetworkIndividual newIndividual = new NetworkIndividual();
newIndividual.network = newNetwork;
networkPopulation.Add(newIndividual);
return(newNetwork);
}
public void ReplacePopulation(List <NeuralNetwork.Network> loadedNetworks)
{
//Clear the population
while (networkPopulation.Count > 0)
{
Destroy(networkPopulation[0].network.gameObject);
networkPopulation.RemoveAt(0);
}
//Load the new population
population = loadedNetworks.Count;
for (int i = 0; i < population; i++)
{
NetworkIndividual newIndividual = new NetworkIndividual();
newIndividual.network = loadedNetworks[i];
loadedNetworks[i].inputLayer = input;
loadedNetworks[i].onOutput.AddListener(output.SetFly);
networkPopulation.Add(newIndividual);
}
}
public IEnumerator RunSingleEvolutionRound()
{
generation++;
//Run the simulation for each individual
for (int i = 0; i < networkPopulation.Count; i++)
{
yield return(StartCoroutine(Simulation.Instance.RunSimulation(networkPopulation[i].network, 0)));
networkPopulation[i].fitness = Simulation.Instance.step;
}
//Sort the list by fitness
networkPopulation.Sort(
delegate(NetworkIndividual p1, NetworkIndividual p2)
{
return(p2.fitness.CompareTo(p1.fitness));
}
);
//Kill the weak individuals
int numberToKill = (int)(population * deathRatio);
Queue <NetworkIndividual> killedIndividual = new Queue <NetworkIndividual>();
for (int i = 0; i < numberToKill; i++)
{
//Some survive by luck
if (Random.Range(0.0f, 1.0f) > 0.05f)
{
killedIndividual.Enqueue(networkPopulation[networkPopulation.Count - 1]);
networkPopulation.RemoveAt(networkPopulation.Count - 1);
}
}
//Calculate the average fitness for ui
float average = 0;
foreach (NetworkIndividual i in networkPopulation)
{
average += i.fitness;
}
average /= networkPopulation.Count;
int mutatedCount = 0;
//Mutate some random individuals
foreach (NetworkIndividual i in networkPopulation)
{
if (mutateChance > Random.Range(0.0f, 1.0f))
{
MutateGene(i.network);
mutatedCount++;
}
}
//Breed
int survivorsCount = networkPopulation.Count;
int childrenToProduce = population - survivorsCount;
for (int i = 0; i < childrenToProduce; i++)
{
//Produce a child with two random survivors
NetworkIndividual child = killedIndividual.Dequeue();
networkPopulation.Add(child);
Breed(networkPopulation[Random.Range(0, survivorsCount)].network, networkPopulation[Random.Range(0, survivorsCount)].network, child.network);
}
//Display an information panel about this generation
infoPanelManager.NewGeneration(generation, networkPopulation[0].fitness, (int)average, mutatedCount, childrenToProduce, networkPopulation.Count);
//Update the curves
averageCurve.AddPoint(average);
bestCurve.AddPoint(networkPopulation[0].fitness);
}
