CarNeuralCore createNewCarObject()
{
GameObject newGameObject = Instantiate(carPrefab);
newGameObject.SetActive(true);
newGameObject.transform.SetParent(this.transform, false);
newGameObject.GetComponent <CarFitness> ().FitnessType = fitnessType;
CarNeuralCore carNeuralCore = newGameObject.GetComponent <CarNeuralCore> ();
carNeuralCore.OnCarDisabled += onCarDisabled;
carNeuralCore.OnCarClicked += onCarClicked;
carNeuralCore.OnExplode += onCarExplode;
carNeuralCore.ExplodeOnDisable = explosions;
cars.Add(carNeuralCore);
return(carNeuralCore);
}
void onAllCarsDisabled()
{
Generation++;
System.Random rnd = new System.Random((int)DateTime.Now.Ticks);
List <CarNeuralCore> sortedCarsByFitness = getCarsSortedByFitness();
List <CarNeuralCore> sortedCarsByDistace = getCarsSortedByDistTravelled();
refreshGhostCarsPositions(sortedCarsByDistace);
double [] fitnesses = new double [sortedCarsByFitness.Count];
List <CarSimpleData> newGenCars = new List <CarSimpleData> ();
newGenCars.Add(sortedCarsByFitness [0].GetCarSimpleData());
for (int i = 0; i < sortedCarsByFitness.Count; i++)
{
fitnesses [i] = sortedCarsByFitness [i].GetComponent <CarFitness> ().Fitness;
}
double avgFitness = getAverage(fitnesses);
if (avgFitness < GlobalConst.MIN_AVG_FITNESS)
{
ResetSimulation();
ActivateCars();
OnNewGenCreated?.Invoke();
return;
}
float prevBestDistance = prevCarWithFurthestDistTravelled.DistTravelled;
float prevBestFitness = (float)prevCarWithHighestFitness.Fitness;
float currentBestFitness = (float)fitnesses [0];
float currentBestDistance = sortedCarsByDistace [0].GetComponent <CarFitness> ().DistanceTravelled;
//Debug.Log ("Avg fitness: " + avgFitness);
if (currentBestFitness > prevCarWithHighestFitness.Fitness)
{
prevCarWithHighestFitness = sortedCarsByFitness [0].GetCarSimpleData();
}
else if (prevCarWithHighestFitness.Weights != null)
{
newGenCars.Add(prevCarWithHighestFitness.GetCopy());
}
if (currentBestDistance > prevCarWithFurthestDistTravelled.DistTravelled)
{
prevCarWithFurthestDistTravelled = sortedCarsByDistace [0].GetCarSimpleData();
}
else if (sortedCarsByDistace [0] != sortedCarsByFitness [0] && prevCarWithFurthestDistTravelled.Weights != null)
{
newGenCars.Add(prevCarWithFurthestDistTravelled.GetCopy());
}
if (AdaptiveMutationProbability)
{
if (prevBestFitness > currentBestFitness)
{
float d = currentBestFitness / prevBestFitness;
d = 1f - d;
mutationProbability += mutationProbabilityD * d;
}
else
{
mutationProbability = DEFAULT_MUTATION_PROBABILITY;
}
//Debug.Log ("prev best: " + prevBestFitness);
//Debug.Log ("current best: " + currentBestFitness);
mutationProbability = Mathf.Clamp(mutationProbability, 0.05f, 0.5f);
}
while (newGenCars.Count < carsCount - newRandomCarsCount)
{
int parent1index = genetics.RouletteSelect(fitnesses);
int parent2index = genetics.RouletteSelect(fitnesses);
int helpIndex = 0;
while (parent1index == parent2index)
{
parent2index = genetics.RouletteSelect(fitnesses);
helpIndex++;
if (helpIndex > carsCount)
{
break;
}
}
CarSimpleData c1;
CarSimpleData c2;
genetics.CrossoverCars(sortedCarsByFitness [parent1index].GetCarSimpleData(), sortedCarsByFitness [parent2index].GetCarSimpleData(), out c1, out c2, Genetics.CrossType.ARYTM);
newGenCars.Add(c1);
newGenCars.Add(c2);
}
for (int i = 0; i < newGenCars.Count; i++)
{
//different approach
//if (rnd.NextDouble () < mutationProbability)
//{
// genetics.Mutation (newGenCars [i].Weights, mutationProbability);
//}
genetics.Mutation(newGenCars [i].Weights, mutationProbability);
}
if (CrossbreedSensors)
{
for (int i = 0; i < newGenCars.Count; i++)
{
if (rnd.NextDouble() < mutationProbability)
{
float d = ((float)rnd.NextDouble() * SENSOR_LENGTH_D) - (SENSOR_LENGTH_D / 2f);
float newLenght = newGenCars [i].SensorsLength + d;
if (newLenght < 0)
{
newLenght = 0;
}
newGenCars [i].SensorsLength = newLenght;
}
else if (rnd.NextDouble() < mutationProbability)
{
float d = ((float)rnd.NextDouble() * ANGLE_BETWEEN_SENSORS_D) - (ANGLE_BETWEEN_SENSORS_D / 2f);
float newAngle = newGenCars [i].AngleBetweenSensors + d;
if (newAngle < 0)
{
newAngle = 0;
}
newGenCars [i].AngleBetweenSensors = newAngle;
}
}
}
while (newGenCars.Count < carsCount)
{
CarSimpleData carSimpleData = new CarSimpleData();
carSimpleData.Weights = sortedCarsByFitness [0].GetRandomNeuralNetworkWeights();
carSimpleData.AngleBetweenSensors = (float)rnd.NextDouble() * MAX_ANGLE_BETWEEN_SENSORS;
carSimpleData.SensorsLength = (float)rnd.NextDouble() * MAX_SENSOR_LENGTH;
newGenCars.Add(carSimpleData);
}
int diff = newGenCars.Count - cars.Count;
if (diff > 0)
{
for (int i = 0; i < diff; i++)
{
CarNeuralCore carNeuralCore = createNewCarObject();
carNeuralCore.Init(currentTopology);
}
}
for (int i = 0; i < newGenCars.Count; i++)
{
cars [i].SetWeights(newGenCars [i].Weights);
if (CrossbreedSensors)
{
cars [i].SetSensorsLength(newGenCars [i].SensorsLength);
cars [i].SetAngleBetweenSensors(newGenCars [i].AngleBetweenSensors);
}
}
diff = cars.Count - newGenCars.Count;
if (diff > 0)
{
for (int i = 0; i < diff; i++)
{
deleteLastCarObject();
}
}
ResetCars();
OnNewGenCreated?.Invoke();
ActivateCars();
}
