private IPopulation initPopulation()
{
IPopulation result = new BasicPopulation(POPULATION_SIZE, null);
BasicSpecies defaultSpecies = new BasicSpecies();
defaultSpecies.Population = result;
for (int i = 0; i < POPULATION_SIZE; i++) {
IntegerArrayGenome genome = RandomGenome();
defaultSpecies.Members.Add(genome);
}
result.GenomeFactory = new IntegerArrayGenomeFactory(cities.Length);
result.Species.Add(defaultSpecies);
return result;
}
/// <summary>
/// Construct a method genetic algorithm.
/// </summary>
/// <param name="phenotypeFactory">The phenotype factory.</param>
/// <param name="calculateScore">The score calculation object.</param>
/// <param name="populationSize">The population size.</param>
public MLMethodGeneticAlgorithm(MLMethodGenomeFactory.CreateMethod phenotypeFactory,
ICalculateScore calculateScore, int populationSize)
: base(TrainingImplementationType.Iterative)
{
// create the population
IPopulation population = new BasicPopulation(populationSize, null);
population.GenomeFactory = new MLMethodGenomeFactory(phenotypeFactory,
population);
ISpecies defaultSpecies = population.CreateSpecies();
for (int i = 0; i < population.PopulationSize; i++)
{
IMLEncodable chromosomeNetwork = (IMLEncodable)phenotypeFactory();
MLMethodGenome genome = new MLMethodGenome(chromosomeNetwork);
defaultSpecies.Add(genome);
}
defaultSpecies.Leader = defaultSpecies.Members[0];
// create the trainer
this.genetic = new MLMethodGeneticAlgorithmHelper(population,
calculateScore);
this.genetic.CODEC = new MLEncodableCODEC();
IGenomeComparer comp = null;
if (calculateScore.ShouldMinimize)
{
comp = new MinimizeScoreComp();
}
else
{
comp = new MaximizeScoreComp();
}
this.genetic.BestComparer = comp;
this.genetic.SelectionComparer = comp;
// create the operators
int s = Math
.Max(defaultSpecies.Members[0].Size / 5, 1);
Genetic.Population = population;
this.genetic.AddOperation(0.9, new Splice(s));
this.genetic.AddOperation(0.1, new MutatePerturb(1.0));
}
