public MelodyGenerator(ICrossoverOperator crossoverOperator, ISimilarityFitnessCalculator fitnessCalculator, IInitializazer initializer, List <IMeasureMutationOperator> measureMutationOperators, List <IMelodyMutationOperator> melodyMutationOperators, IReplacementOperator replacementOperator, ISelector selector, IStoppingCriterionChecker stopChecker)
{
_crossoverOperator = crossoverOperator;
_fitnessCalculator = fitnessCalculator;
_initializer = initializer;
_measureMutationOperators = measureMutationOperators;
_melodyMutationOperators = melodyMutationOperators;
_replacementOperator = replacementOperator;
_selector = selector;
_stopChecker = stopChecker;
Generations = new List <Population>();
}
public SteadyStateGeneticAlgorithm(
IProblem <T> problem,
int populationSize,
ISelectionOperator <T> selectionOperator,
ICrossoverOperator <T> crossoverOperator,
IMutationOperator <T> mutationOperator,
ITerminationCondition terminationCondition,
double generationGap,
IReplacementOperator <T> replacementOperator) :
base(problem, populationSize, selectionOperator,
crossoverOperator, mutationOperator, terminationCondition)
{
_generationGap = generationGap;
_replacementOperator = replacementOperator;
}
public GeneticAlgorithmBuilder(
IProblem <T> problem,
ICrossoverOperator <T> crossoverOperator,
IMutationOperator <T> mutationOperator)
{
_problem = problem;
_crossoverOperator = crossoverOperator;
_mutationOperator = mutationOperator;
_selectionOperator = new TournamentSelectionOperator <T>(3, problem);
_replacementOperator = new WorstFitnessReplacementOperator <T>(problem);
_populationSize = 1000;
_maxGenerations = 1000;
_maxEvaluations = 100000;
_fitnessValue = 10e-6;
_elitismRate = 1.0 / _populationSize;
_generationGap = 0.4;
_variant = GeneticAlgorithmVariant.SteadyState;
}
public GeneticAlgorithmBuilder <T> SetReplacementOperator(IReplacementOperator <T> replacementOperator)
{
_replacementOperator = replacementOperator;
return(this);
}
