private int startPopulationSize; //The starting population size of the genetic algorithm
#endregion Fields
#region Constructors
public NNPopulation(int startingPopulationSize, int[] neuralNetworkLayerSizes, Type simulationType)
{
//Populate the input variables
networkStructure = neuralNetworkLayerSizes;
startPopulationSize = startingPopulationSize;
//Generate the hidden layer structure from the input network structure
int[] hiddenLayers = new int[neuralNetworkLayerSizes.Length - 2];
for (int i = 0; i < hiddenLayers.Length; i++)
hiddenLayers[i] = neuralNetworkLayerSizes[i + 1];
hiddenLayerStructure = hiddenLayers;
//Create the population list
population = new List<NN>();
//Generate nerual networks for the genetic algorithm to use to compete
for (int i = 0; i < startingPopulationSize; i++)
population.Add(new NN(neuralNetworkLayerSizes[0], neuralNetworkLayerSizes[neuralNetworkLayerSizes.Length - 1], neuralNetworkLayerSizes.Length - 2, hiddenLayers));
this.simulationType = simulationType;
this.showSimulation = true;
this.breedingFunction = BreedingFunction.Crossover;
this.numberOfGenerations = 0;
//Create an instance of a NNPopulationSimulation of the specified type
simulation = (NNPopulationSimulation)Activator.CreateInstance(simulationType, new object[] { population, numFitnessSimulationIterations, numberOfElites * numOfCopiesOfElites });
}
public void Update(GameTime gameTime)
{
if (showSimulation)
//Show the simulation (the simulation may choose to not perform any operations on this call
simulation.ShowSimulation();
simulation.Update(gameTime);
if (simulation.IsSimulationComplete())
{
numberOfGenerations++;
if (showSimulation)
//Close the simulation if it requires that
simulation.CloseSimulation();
latestFitness = simulation.GetSimulationResults();
KeyValuePair<int, double>[] sortedFitnessList = GetSortedFitnessList(latestFitness);
//Select the breeding couples given their fitness
Tuple<int, int>[] breedingCouples = SelectCouplesToBreed(sortedFitnessList);
//Breed the selected couples
if (breedingFunction == BreedingFunction.Average)
population = BreedAverage(breedingCouples, sortedFitnessList);
else if (breedingFunction == BreedingFunction.Crossover)
population = BreedCrossover(breedingCouples, sortedFitnessList);
else if (breedingFunction == BreedingFunction.PickEach)
population = BreedPickEach(breedingCouples, sortedFitnessList);
//Mutate the new population
Mutate(probabilityOfWeightMutationIfChosenToMutate, (int)(((double)population.Count) * minimumProportionOfMutationsPerPopulationMember), (int)(((double)population.Count) * maximumProportionOfMutationsPerPopulationMember), rangeOfMutationPerturbation);
//Create an instance of a NNPopulationSimulation of the specified type
simulation = (NNPopulationSimulation)Activator.CreateInstance(simulationType, new object[] { population, numFitnessSimulationIterations, numberOfElites * numOfCopiesOfElites });
}
}
