// Create a new generation
public void Generate()
{
NeuralNetwork[] temp = new NeuralNetwork[cars.Count];
// Refill the population with children from the mating pool
for (int i = 0; i < cars.Count - 1; i++)
{
if (i == bestCar)
{
temp[i] = cars[i].GetComponent <CarDNA>().neuralNetwork;
}
else
{
CarDNA partnerA = PickOne(cars);
CarDNA partnerB = PickOne(cars);
NeuralNetwork child = partnerA.CrossOver(partnerB);
Mutate(ref child, mutationRate);
temp[i] = child;
}
}
for (int i = 0; i < cars.Count - 1; i++)
{
cars[i].GetComponent <CarDNA>().neuralNetwork = temp[i];
}
cars[cars.Count - 1].GetComponent <CarDNA>().neuralNetwork = new NeuralNetwork(inputNodes, hiddenNodes, outputNodes);
//if (matingPool.Count != 0)
{
generations++;
}
RestartCars(spawnPosition);
}
void Crossover()
{
for (int i = 30; i < 101; i++)
{
List <CarDNA> parent_DNA = new List <CarDNA>();
parent1 = Random.Range(0, 30);
do
{
parent2 = Random.Range(0, 30);
} while (parent1 == parent2);
parent_DNA.Add(PopulationCopy[parent1]);
parent_DNA.Add(PopulationCopy[parent2]);
CarDNA Child = new CarDNA();
Child.model = new List <DNA>();
for (int j = 0; j < NumberOfPositions - 1; j++)
{
DNA child = new DNA();
int num = Random.Range(0, 2);
child.position = parent_DNA[num].model[j].position;
Child.model.Add(child);
}
Population.Add(Child);
Population[i].fitness = -1000.0;
}
}
// Start is called before the first frame update
void Start()
{
rigidbody = GetComponent <Rigidbody>();
carDNA = GetComponent <CarDNA>();
headObject = transform.Find("Head");
//TEST
//carDNA.InitCar(5, 5, 2);
}
void CreatePopulation()
{
double distance = System.Math.Sqrt(System.Math.Pow(transform.position.z - 6.23, 2));
double distance_all = System.Math.Sqrt(System.Math.Pow(transform.position.z - 6.23, 2) + System.Math.Pow(transform.position.x + 12.68, 2));
CarDNA entity = new CarDNA();
entity.model = new List <DNA>();
entity.fitness = -distance * 0.2 - distance_all * 0.8;
CarPositions.ForEach(x => entity.model.Add(x));
Population.Add(entity);
}
void BestCars_fun()
{
CarDNA entity = new CarDNA();
entity.model = new List <DNA>();
entity.fitness = 200 - Car_count;
CarPositions.ForEach(x => entity.model.Add(x));
BestCars.Add(entity);
Population.Add(entity);
gameObject.SetActive(false);
}
// Crossover
public NeuralNetwork CrossOver(CarDNA partner)
{
// A new child
NeuralNetwork child = new NeuralNetwork(neuralNetwork.inputNodes, neuralNetwork.hiddenNodes, neuralNetwork.outputNodes);
//Do crossover
DoCrossOver(ref child.ihWeights, neuralNetwork.ihWeights, partner.neuralNetwork.ihWeights);
//for(int i = 0; i < neuralNetwork.ihWeights.rowNb; i++)
//{
// for(int j = 0; j < neuralNetwork.ihWeights.columnNb; j++)
// {
// child.ihWeights[i][j] = Random.Range(0, 3) == 0 ? neuralNetwork.ihWeights[i][j] : partner.neuralNetwork.ihWeights[i][j];
// }
//}
DoCrossOver(ref child.hoWeights, neuralNetwork.hoWeights, partner.neuralNetwork.hoWeights);
//for (int i = 0; i < neuralNetwork.hoWeights.rowNb; i++)
//{
// for (int j = 0; j < neuralNetwork.hoWeights.columnNb; j++)
// {
// child.hoWeights[i][j] = Random.Range(0, 3) == 0 ? neuralNetwork.hoWeights[i][j] : partner.neuralNetwork.hoWeights[i][j];
// }
//}
DoCrossOver(ref child.biasH, neuralNetwork.biasH, partner.neuralNetwork.biasH);
//for (int i = 0; i < neuralNetwork.biasH.rowNb; i++)
//{
// for (int j = 0; j < neuralNetwork.biasH.columnNb; j++)
// {
// child.biasH[i][j] = Random.Range(0, 3) == 0 ? neuralNetwork.biasH[i][j] : partner.neuralNetwork.biasH[i][j];
// }
//}
DoCrossOver(ref child.biasO, neuralNetwork.biasO, partner.neuralNetwork.biasO);
//for (int i = 0; i < neuralNetwork.biasO.rowNb; i++)
//{
// for (int j = 0; j < neuralNetwork.biasO.columnNb; j++)
// {
// child.biasO[i][j] = Random.Range(0, 3) == 0 ? neuralNetwork.biasO[i][j] : partner.neuralNetwork.biasO[i][j];
// }
//}
return(child);
}