/// <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));
}
/// <summary>
/// Construct an EA.
/// </summary>
/// <param name="thePopulation">The population.</param>
/// <param name="theScoreFunction">The score function.</param>
public BasicEA(IPopulation thePopulation,
ICalculateScore theScoreFunction)
{
RandomNumberFactory = EncogFramework.Instance.RandomFactory.FactorFactory();
EliteRate = 0.3;
MaxTries = 5;
MaxOperationErrors = 500;
CODEC = new GenomeAsPhenomeCODEC();
Population = thePopulation;
ScoreFunction = theScoreFunction;
Selection = new TournamentSelection(this, 4);
Rules = new BasicRuleHolder();
// set the score compare method
if (theScoreFunction.ShouldMinimize)
{
SelectionComparer = new MinimizeAdjustedScoreComp();
BestComparer = new MinimizeScoreComp();
}
else
{
SelectionComparer = new MaximizeAdjustedScoreComp();
BestComparer = new MaximizeScoreComp();
}
// set the iteration
foreach (ISpecies species in thePopulation.Species)
{
foreach (IGenome genome in species.Members)
{
IterationNumber = Math.Max(IterationNumber,
genome.BirthGeneration);
}
}
// Set a best genome, just so it is not null.
// We won't know the true best genome until the first iteration.
if (Population.Species.Count > 0 && Population.Species[0].Members.Count > 0)
{
BestGenome = Population.Species[0].Members[0];
}
}