/// <summary>
/// Construct a neural genetic algorithm.
/// </summary>
/// <param name="network">The network to base this on.</param>
/// <param name="randomizer">The randomizer used to create this initial population.</param>
/// <param name="calculateScore">The score calculation object.</param>
/// <param name="populationSize">The population size.</param>
/// <param name="mutationPercent">The percent of offspring to mutate.</param>
/// <param name="percentToMate">The percent of the population allowed to mate.</param>
public NeuralGeneticAlgorithm(BasicNetwork network,
IRandomizer randomizer,
ICalculateScore calculateScore,
int populationSize, double mutationPercent,
double percentToMate)
{
this.Helper = new NeuralGeneticAlgorithmHelper();
this.Helper.CalculateScore = new GeneticScoreAdapter(calculateScore);
IPopulation population = new BasicPopulation(populationSize);
Helper.MutationPercent = mutationPercent;
Helper.MatingPopulation = (percentToMate * 2);
Helper.PercentToMate = (percentToMate);
Helper.Crossover = (new Splice(network.Structure.CalculateSize() / 3));
Helper.Mutate = (new MutatePerturb(4.0));
Helper.Population = (population);
for (int i = 0; i < population.PopulationSize; i++)
{
BasicNetwork chromosomeNetwork = (BasicNetwork)network
.Clone();
randomizer.Randomize(chromosomeNetwork);
NeuralGenome genome =
new NeuralGenome(this, chromosomeNetwork);
Helper.PerformScoreCalculation(genome);
Helper.Population.Add(genome);
}
population.Sort();
}
