//Run a generation given a particular fitness simulation, this simulation must implement NNPopulationSimulation
public void RunGeneration(Type simulationType, bool showSimulation, BreedingFunction function)
{
//Calculate the fitnesses of a given simiulation, this simulation must implement NNPopulationSimulation
latestFitness = CalculateFitness(simulationType, showSimulation);
KeyValuePair<int, double>[] sortedFitnessList = GetSortedFitnessList(latestFitness);
//Select the breeding couples given their fitness
Tuple<int, int>[] breedingCouples = SelectCouplesToBreed(sortedFitnessList);
//Breed the selected couples
if(function == BreedingFunction.Average)
population = BreedAverage(breedingCouples, sortedFitnessList);
else if(function == BreedingFunction.Crossover)
population = BreedCrossover(breedingCouples, sortedFitnessList);
else if(function == BreedingFunction.PickEach)
population = BreedPickEach(breedingCouples, sortedFitnessList);
//Mutate the new population
Mutate(Params.probabilityOfWeightMutationIfChosenToMutate,
(int)(((double)population.Count) * Params.minimumProportionOfMutationsPerPopulationMember),
(int)(((double)population.Count) * Params.maximumProportionOfMutationsPerPopulationMember),
Params.rangeOfMutationPerturbation);
}
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 });
}
internal static void FillListFromBreedingPop(List <T> nextGen, List <T> BreedingPop, int targetSize, BreedingFunction func)
{
int elitismNum = (int)(OptoGlobals.ElitismPercent * targetSize);
while (nextGen.Count < targetSize)
{
int j = OptoGlobals.RNG.Next(0, elitismNum), k = OptoGlobals.RNG.Next(0, BreedingPop.Count);
while (k == j)
{
k = OptoGlobals.RNG.Next(0, BreedingPop.Count);
}
foreach (T newGuy in func(nextGen[j], BreedingPop[k]))
{
nextGen.Add(newGuy);
}
}
while (nextGen.Count > targetSize)
{
nextGen.RemoveAt(nextGen.Count - 1);
}
}
