private static void Lab73(int?seed = null)
{
string filename = "lab73_order3.txt";
IEvaluation <bool> evaluation = new CBinaryStandardDeceptiveConcatenationEvaluation(3, 100);
RunningTimeStopCondition stopCondition = new RunningTimeStopCondition(evaluation.dMaxValue, 30);
BinaryRandomGenerator generator = new BinaryRandomGenerator(evaluation.pcConstraint, seed);
OnePointCrossover crossover = new OnePointCrossover(0.5, seed);
BinaryBitFlipMutation mutation = new BinaryBitFlipMutation(1.0 / evaluation.iSize, evaluation, seed);
TournamentSelection selection = new TournamentSelection(2, seed);
GAwithDSM ga = new GAwithDSM(evaluation, stopCondition, generator, selection, crossover, mutation, 50);
ga.Run();
SaveDSMs(ga.dsms, filename);
filename = "lab73_order10.txt";
evaluation = new CBinaryBimodalDeceptiveConcatenationEvaluation(10, 100);
stopCondition = new RunningTimeStopCondition(evaluation.dMaxValue, 30);
generator = new BinaryRandomGenerator(evaluation.pcConstraint, seed);
crossover = new OnePointCrossover(0.5, seed);
mutation = new BinaryBitFlipMutation(1.0 / evaluation.iSize, evaluation, seed);
selection = new TournamentSelection(2, seed);
ga = new GAwithDSM(evaluation, stopCondition, generator, selection, crossover, mutation, 50);
ga.Run();
SaveDSMs(ga.dsms, filename);
}
private static Experiment <bool> PrepareExperiment7(int evaluationType, int algorithm, int variables, int selectionMethod, int crossoverMethod, double crossoverProb, double mutationProb, int populationSize, int?seed = null)
{
IEvaluation <bool> evaluation;
Dictionary <string, string> info = new Dictionary <string, string>();
switch (evaluationType)
{
case 0:
evaluation = new CBinaryStandardDeceptiveConcatenationEvaluation(3, variables);
info["problem"] = "order3standard";
break;
case 1:
evaluation = new CBinaryBimodalDeceptiveConcatenationEvaluation(10, variables);
info["problem"] = "order10bimodal";
break;
case 2:
evaluation = new ShuffledStandardDeceptiveConcatenationEvaluation(3, variables);
info["problem"] = "order3shuffled";
break;
case 3:
evaluation = new ShuffledBimodalDeceptiveConcatenationEvaluation(10, variables);
info["problem"] = "order10shuffled";
break;
default:
throw new ArgumentOutOfRangeException();
}
info["variables"] = variables.ToString();
RunningTimeStopCondition stopCondition = new RunningTimeStopCondition(evaluation.dMaxValue, 30);
BinaryRandomGenerator generator = new BinaryRandomGenerator(evaluation.pcConstraint, seed);
ASelection selection;
switch (selectionMethod)
{
case 0:
selection = new RouletteWheelSelection(seed);
info["selection_method"] = "roulette";
break;
case 1:
selection = new TournamentSelection(2, seed);
info["selection_method"] = "tournament";
break;
default:
throw new ArgumentOutOfRangeException();
}
ACrossover crossover;
info["crossover_prob"] = crossoverProb.ToString();
switch (crossoverMethod)
{
case 0:
crossover = new OnePointCrossover(crossoverProb, seed);
info["crossover_method"] = "OnePoint";
break;
case 1:
crossover = new UniformCrossover(crossoverProb, seed);
info["crossover_method"] = "uniform";
break;
case 2:
crossover = new DSMCrossover(crossoverProb, seed);
info["crossover_method"] = "withDSM";
break;
default:
throw new ArgumentOutOfRangeException();
}
mutationProb = mutationProb / evaluation.iSize;
info["mutation_prob"] = mutationProb.ToString();
BinaryBitFlipMutation mutation = new BinaryBitFlipMutation(mutationProb, evaluation, seed);
info["population"] = populationSize.ToString();
GeneticAlgorithm <bool> ga;
switch (algorithm)
{
case 0:
ga = new GeneticAlgorithm <bool>(evaluation, stopCondition, generator, selection, crossover, mutation, populationSize);
info["algorithm"] = "GA";
break;
case 1:
ga = new GAwithDSM(evaluation, stopCondition, generator, selection, crossover, mutation, populationSize, 10);
info["algorithm"] = "GAwithDSM";
break;
default:
throw new ArgumentOutOfRangeException();
}
Experiment <bool> experiment = new Experiment <bool>(ga, info, ExperimentFinished);
return(experiment);
}
