/// <inheritdoc/>
public IMLMethod Decode(IGenome genome)
{
MLMethodGenome genome2 = (MLMethodGenome)genome;
genome2.Decode();
return(genome2.Phenotype);
}
/// <inheritdoc/>
public IGenome Factor()
{
IGenome result = new MLMethodGenome(
(IMLEncodable)this.factory());
result.Population = this.population;
return result;
}
/// <inheritdoc/>
public IGenome Factor(IGenome other)
{
MLMethodGenome result = (MLMethodGenome)Factor();
result.Copy(other);
result.Population = this.population;
return(result);
}
/// <inheritdoc/>
public IGenome Factor()
{
IGenome result = new MLMethodGenome(
(IMLEncodable)this.factory());
result.Population = this.population;
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));
}
/// <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));
}
