| public NeuralGeneticAlgorithm ( BasicNetwork network, IRandomizer randomizer, ICalculateScore calculateScore, int populationSize, double mutationPercent, double percentToMate ) : Encog.MathUtil.Randomize | ||
| network | BasicNetwork | The network to base this on. |
| randomizer | IRandomizer | The randomizer used to create this initial population. |
| calculateScore | ICalculateScore | The score calculation object. |
| populationSize | int | The population size. |
| mutationPercent | double | The percent of offspring to mutate. |
| percentToMate | double | The percent of the population allowed to mate. |
| return | Encog.MathUtil.Randomize | |
public NeuralGeneticAlgorithm(BasicNetwork network,
IRandomizer randomizer, ICalculateScore calculateScore,
int populationSize, double mutationPercent,
double percentToMate) : base(TrainingImplementationType.Iterative)
{
Genetic = new NeuralGeneticAlgorithmHelper
{
CalculateScore = new GeneticScoreAdapter(calculateScore)
};
IPopulation population = new BasicPopulation(populationSize);
Genetic.MutationPercent = mutationPercent;
Genetic.MatingPopulation = percentToMate*2;
Genetic.PercentToMate = percentToMate;
Genetic.Crossover = new Splice(network.Structure.CalculateSize()/3);
Genetic.Mutate = new MutatePerturb(4.0d);
Genetic.Population = population;
for (int i = 0; i < population.PopulationSize; i++)
{
var chromosomeNetwork = (BasicNetwork) (network
.Clone());
randomizer.Randomize(chromosomeNetwork);
var genome = new NeuralGenome(chromosomeNetwork) {GA = Genetic};
Genetic.PerformCalculateScore(genome);
Genetic.Population.Add(genome);
}
population.Sort();
}
