private static void Lab5Test(int?seed)
{
var instance = EBinaryKnapsackInstance.knapPI_1_100_1000_1;
CBinaryKnapsackEvaluation evaluation = new CheckingBinaryKnapsackEvaluation(instance);
IterationsStopCondition stopCondition = new IterationsStopCondition(evaluation.dMaxValue, 100);
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);
GeneticAlgorithm <bool> ga = new GeneticAlgorithm <bool>(evaluation, stopCondition, generator, selection, crossover, mutation, 50);
ga.Run();
ReportOptimizationResult(ga.Result);
evaluation = new CheckingBinaryKnapsackEvaluation(instance);
stopCondition = new IterationsStopCondition(evaluation.dMaxValue, 100);
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 LamarckGeneticAlgorithm(evaluation, stopCondition, generator, selection, crossover, mutation, 50);
ga.Run();
ReportOptimizationResult(ga.Result);
evaluation = new CheckingBinaryKnapsackEvaluation(instance);
stopCondition = new IterationsStopCondition(evaluation.dMaxValue, 100);
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 BaldwinGeneticAlgorithm(evaluation, stopCondition, generator, selection, crossover, mutation, 50);
ga.Run();
ReportOptimizationResult(ga.Result);
evaluation = new PenalizingBinaryKnapsackEvaluation(instance);
stopCondition = new IterationsStopCondition(evaluation.dMaxValue, 100);
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 GeneticAlgorithm <bool>(evaluation, stopCondition, generator, selection, crossover, mutation, 50);
ga.Run();
ReportOptimizationResult(ga.Result);
evaluation = new PenalizingBinaryKnapsackEvaluation(instance);
stopCondition = new IterationsStopCondition(evaluation.dMaxValue, 100);
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 LamarckGeneticAlgorithm(evaluation, stopCondition, generator, selection, crossover, mutation, 50);
ga.Run();
ReportOptimizationResult(ga.Result);
evaluation = new PenalizingBinaryKnapsackEvaluation(instance);
stopCondition = new IterationsStopCondition(evaluation.dMaxValue, 100);
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 BaldwinGeneticAlgorithm(evaluation, stopCondition, generator, selection, crossover, mutation, 50);
ga.Run();
ReportOptimizationResult(ga.Result);
}
private static Experiment <bool> PrepareExperiment5(int evaluationType, int instance, int algorithm, int selectionMethod, int crossoverMethod, double crossoverProb, double mutationProb, int populationSize, int?seed = null)
{
CBinaryKnapsackEvaluation evaluation;
Dictionary <string, string> info = new Dictionary <string, string>();
List <string> instances = new List <string>
{
"f1_l_d_kp_10_269",
"f2_l_d_kp_20_878",
"f3_l_d_kp_4_20",
"f4_l_d_kp_4_11",
"f5_l_d_kp_15_375",
"f6_l_d_kp_10_60",
"f7_l_d_kp_7_50",
"f8_l_d_kp_23_10000",
"f9_l_d_kp_5_80",
"f10_l_d_kp_20_879",
"knapPI_1_100_1000_1",
"knapPI_1_200_1000_1",
"knapPI_1_500_1000_1",
"knapPI_1_1000_1000_1",
"knapPI_1_2000_1000_1",
"knapPI_1_5000_1000_1",
"knapPI_1_10000_1000_1",
"knapPI_2_100_1000_1",
"knapPI_2_200_1000_1",
"knapPI_2_500_1000_1",
"knapPI_2_1000_1000_1",
"knapPI_2_2000_1000_1",
"knapPI_2_5000_1000_1",
"knapPI_2_10000_1000_1",
"knapPI_3_100_1000_1",
"knapPI_3_200_1000_1",
"knapPI_3_500_1000_1",
"knapPI_3_1000_1000_1",
"knapPI_3_2000_1000_1",
"knapPI_3_5000_1000_1",
"knapPI_3_10000_1000_1"
};
info["instance"] = instances[instance];
switch (evaluationType)
{
case 0:
evaluation = new CBinaryKnapsackEvaluation((EBinaryKnapsackInstance)instance);
info["evaluation"] = "base";
break;
case 1:
evaluation = new CheckingBinaryKnapsackEvaluation((EBinaryKnapsackInstance)instance);
info["evaluation"] = "checking";
break;
case 2:
evaluation = new NegativeBinaryKnapsackEvaluation((EBinaryKnapsackInstance)instance);
info["evaluation"] = "negative";
break;
case 3:
evaluation = new PenalizingBinaryKnapsackEvaluation((EBinaryKnapsackInstance)instance);
info["evaluation"] = "penalizing";
break;
default:
throw new ArgumentOutOfRangeException();
}
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;
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 KnapsackGeneticAlgorithm(evaluation, stopCondition, generator, selection, crossover, mutation, populationSize);
info["algorithm"] = "Checking";
break;
case 2:
ga = new LamarckGeneticAlgorithm(evaluation, stopCondition, generator, selection, crossover, mutation, populationSize);
info["algorithm"] = "Lamarck";
break;
case 3:
ga = new BaldwinGeneticAlgorithm(evaluation, stopCondition, generator, selection, crossover, mutation, populationSize);
info["algorithm"] = "Baldwin";
break;
default:
throw new ArgumentOutOfRangeException();
}
Experiment <bool> experiment = new Experiment <bool>(ga, info, ExperimentFinished);
return(experiment);
}
