public bool Execute()
{
ga.Crossover = new CustomCrossover();
ga.GenomeFactory = new CustomFactory(0, parameters.Questoes.Count - 1, parameters.QtdQuestoes);
switch (parameters.Seletor)
{
case SeletorGA.Roleta:
ga.Selector = new WeightedSelector(ga.Genomes);
break;
case SeletorGA.Sequencial:
ga.Selector = new SequentialSelector(ga.Genomes);
break;
case SeletorGA.Randomico:
ga.Selector = new RandomSelector(ga.Genomes);
break;
}
ga.Crossover.CrossoverProbability = parameters.ProbabilidadeReproducao / 100.0; //0.3;0.2;
ga.Crossover.MutationProbability = parameters.ProbabilidadeMutacao / 100.0; //0.015;
ga.Elitism = parameters.HabilitaElitismo;
ga.CustomObj = parameters.Questoes;
if (OnGANewGeneration != null)
{
ga.NewGeneration += new GeneticAlgorithmEventHandler(this.OnNewGeneration);
}
if (OnGANewBestFitness != null)
{
ga.NewBestFitness += new GeneticAlgorithmGenomeEventHandler(this.OnNewBestFitness);
}
ga.QryBestFitness += new GeneticAlgorithmCancelEventHandler(this.OnQryBestFitness);
ga.Evaluator = new CustomEvaluator(parameters);
ga.CreateGenomes(9);
ga.ExitConditions.Duration = TimeSpan.FromSeconds(parameters.MaxDuration);
ga.ExitConditions.Generations = parameters.MaxGenerations;
ga.ExitConditions.FitnessGoal = parameters.MinFitnessGoal;
bestGenome = (CustomGenome)ga.Execute();
stopTime = DateTime.Now;
exitCondiction = ga.ExitConditions.ExitCondiction;
bestGenome.UpdateStat(parameters);
return(true);
}
