public void Configure(IFitness fItness, IMutate mutate, ICrossover crossover, ISelection selection)
{
Mutate = mutate;
Crossover = crossover;
Fitness = fItness;
Selection = selection;
}
public GeneticAlgorithmBuilder <U> SetMutator(IMutate <U> mutator)
{
Contract.Requires <ArgumentNullException>(mutator != null, "Argument cannot be null.");
Mutator = mutator;
return(this);
}
public EliminationGeneticAlgorithm(IMutate <Chromosome> mutation, ISelection <Chromosome> selection, ICrossover <Chromosome> crossover,
IFitnessFunction <Chromosome> fitnessFunction, int iterationLimit, double fitnessTerminator, int populationSize, int chromosomeSize)
: base(mutation, selection, crossover, fitnessFunction, iterationLimit, fitnessTerminator, populationSize)
{
Population = new Population <Chromosome>(populationSize);
ChromosomeSize = chromosomeSize;
InitializePopulation();
}
protected GeneticAlgorithm(IMutate <T> mutation, ISelection <T> selection, ICrossover <T> crossover,
IFitnessFunction <T> fitnessFunction, int iterationLimit, double fitnessTerminator, int populationSize)
{
Mutation = mutation;
Selection = selection;
Crossover = crossover;
FitnessFunction = fitnessFunction;
IterationLimit = iterationLimit;
FitnessTerminator = fitnessTerminator;
PopulationSize = populationSize;
}
/// <summary>
/// Creates a new population.
/// </summary>
/// <param name="population">The current population.</param>
/// <param name="elite">The elite operator.</param>
/// <param name="mutate">The mutate operator.</param>
/// <param name="crossover">The crossover operator.</param>
/// <param name="diversify">The diversify operator.</param>
/// <returns></returns>
public Population CreateNewPopulation(Population population, IElite elite, IMutate mutate, ICrossOver crossover, IDiversify diversify)
{
IChromosome parent1, parent2;
IChromosome child1 = null, child2 = null;
// create a new population
var nextPopulation = new Population();
// copy all elites to the new population
elite?.Process(population, nextPopulation);
// make sure the population is diversified enough
diversify?.Process(nextPopulation, population.Count);
// while next population is not filled completely
while (nextPopulation.Count < population.Count)
{
// select 2 parents
DoTournament(population, out parent1, out parent2);
// perform crossover so we get 2 children
crossover?.Process(parent1, parent2, out child1, out child2);
// mutate both children
mutate?.Process(child1);
mutate?.Process(child2);
// and add the children to the next population
nextPopulation.Add(child1);
if (nextPopulation.Count < population.Count)
{
nextPopulation.Add(child2);
}
}
return(nextPopulation);
}
public static IFinder GetGAImplementation(ICrossover crossover = null, IMutate mutate = null, IFitness fitness = null, ISelection selection = null, int populationSize = 0, int generationLimit = 0, int solutionsToPick = 0)
{
var result = new GAFinder();
result.Configure(fitness, mutate, crossover, selection);
if (populationSize > 0)
{
result.PopulationSize = populationSize;
}
if (generationLimit > 0)
{
result.GenerationLimit = generationLimit;
}
if (solutionsToPick > 0)
{
result.BestSolutionToPick = solutionsToPick;
}
return(result);
}
/// <summary>
/// Adds the specified mutate operator
/// </summary>
/// <param name="elite">The mutate operator</param>
public void Add(IMutate mutate)
{
_mutate = mutate;
}
