private List <String> GreedyKnapsack(int?seed, bool penalized, EffectType type)
{
List <String> results = new List <String>();
var evaluations = GenerateProblems();
foreach (var evaluation in evaluations)
{
((CBinaryKnapsackEvaluation)evaluation).bSetPenalized(penalized);
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);
KnapsackGA ga = new KnapsackGA(((CBinaryKnapsackEvaluation)evaluation), stopCondition, generator, selection, crossover, mutation, 50, type, seed);
ga.Run();
results.Add(FormatSave(ga));
ReportOptimizationResult(ga.Result, debug: true);
}
return(results);
}
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);
}
public MutatedIslandGA(IEvaluation <bool> evaluation, AStopCondition stopCondition, AGenerator <bool> generator,
ASelection selection, ACrossover crossover, IMutation <bool> mutation, int populationSize)
: base(evaluation, stopCondition, generator, selection, crossover, mutation, populationSize)
{
var moreMutation = new BinaryBitFlipMutation(1, evaluation);
int mutatedPop = populationSize / 10 % 2 == 1 ? populationSize / 10 - 1 : populationSize / 10;
mutatedPop = mutatedPop == 0 ? 2 : mutatedPop;
mutantIsland = new GeneticAlgorithm <bool>(evaluation, stopCondition, generator, selection, crossover, moreMutation, mutatedPop);
mutantIsland.Initialize();
}
private static void Lab4BinaryGA(IEvaluation <bool> evaluation, ASelection selection, ACrossover crossover, int?seed)
{
IterationsStopCondition stopCondition = new IterationsStopCondition(evaluation.dMaxValue, 100);
BinaryRandomGenerator generator = new BinaryRandomGenerator(evaluation.pcConstraint, seed);
BinaryBitFlipMutation mutation = new BinaryBitFlipMutation(1.0 / evaluation.iSize, evaluation, seed);
GeneticAlgorithm <bool> ga = new GeneticAlgorithm <bool>(evaluation, stopCondition, generator, selection, crossover, mutation, 50);
ga.Run();
ReportOptimizationResult(ga.Result);
}
private static void Lab4BinaryMutatedGA(IEvaluation <bool> evaluation, ASelection selection, ACrossover crossover, int?seed)
{
IterationsStopCondition stopCondition = new IterationsStopCondition(evaluation.dMaxValue, 100);
BinaryRandomGenerator generator = new BinaryRandomGenerator(evaluation.pcConstraint, seed);
BinaryBitFlipMutation mutation = new BinaryBitFlipMutation(0, evaluation, seed);
MutatedIslandGA miga = new MutatedIslandGA(evaluation, stopCondition, generator, selection, crossover, mutation, 20);
miga.Run();
ReportOptimizationResult(miga.Result);
}
private String Lab4BinaryGA(IEvaluation <bool> evaluation, ASelection selection, ACrossover crossover, double pMut, int popSize, int?seed)
{
IterationsStopCondition stopCondition = new IterationsStopCondition(evaluation.dMaxValue, 100);
BinaryRandomGenerator generator = new BinaryRandomGenerator(evaluation.pcConstraint, seed);
BinaryBitFlipMutation mutation = new BinaryBitFlipMutation(pMut, evaluation, seed);
GeneticAlgorithm <bool> ga = new GeneticAlgorithm <bool>(evaluation, stopCondition, generator, selection, crossover, mutation, 20);
ga.Run();
return(FormatSave(ga));
//ReportOptimizationResult(ga.Result);
}
private String Lab4BinaryGaMutationCrossover(IEvaluation <bool> evaluation, double pMut, double pCro, int?seed)
{
IterationsStopCondition stopCondition = new IterationsStopCondition(evaluation.dMaxValue, 500);
BinaryRandomGenerator generator = new BinaryRandomGenerator(evaluation.pcConstraint, seed);
BinaryBitFlipMutation mutation = new BinaryBitFlipMutation(pMut, evaluation, seed);
OnePointCrossover crossover = new OnePointCrossover(pCro, seed);
TournamentSelection selection = new TournamentSelection(5, seed);
GeneticAlgorithm <bool> ga = new GeneticAlgorithm <bool>(evaluation, stopCondition, generator, selection, crossover, mutation, 50);
ga.Run();
return(FormatSave(ga));
}
private String Lab6BinaryGA(IEvaluation <bool> evaluation, ASelection selection, ACrossover crossover, double pMut, int popSize, Dictionary <String, int> problemStallCounter, int?seed)
{
IterationsStopCondition stopCondition = new IterationsStopCondition(evaluation.dMaxValue, 100);
BinaryRandomGenerator generator = new BinaryRandomGenerator(evaluation.pcConstraint, seed);
BinaryBitFlipMutation mutation = new BinaryBitFlipMutation(pMut, evaluation, seed);
GAStallDetection <bool> ga = new GAStallDetection <bool>(evaluation, stopCondition, generator, selection, crossover, mutation, popSize, 20);
ga.Run();
//Console.WriteLine(evaluation.GetType().Name + " : " + ga.countStalling);
DictionaryCounterUpdate(problemStallCounter, evaluation.GetType().Name, ga.countStalling);
//ReportOptimizationResult(ga.Result);
return(FormatSave(ga));
}
private static void Lab5(int?seed)
{
CBinaryKnapsackEvaluation evaluation = new CBinaryKnapsackEvaluation(EBinaryKnapsackInstance.knapPI_1_100_1000_1);
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);
}
private static void Lab8BiObjectiveBinaryGA(IEvaluation <bool, Tuple <double, double> > evaluation, int?seed)
{
RunningTimeStopCondition stopCondition = new RunningTimeStopCondition(5);
DefaultDominationComparer dominationComparer = new DefaultDominationComparer();
BinaryRandomGenerator generator = new BinaryRandomGenerator(evaluation.pcConstraint, seed);
OnePointCrossover crossover = new OnePointCrossover(0.5, seed);
BinaryBitFlipMutation mutation = new BinaryBitFlipMutation(1.0 / evaluation.iSize, evaluation, seed);
SampleBiObjectiveSelection selection = new SampleBiObjectiveSelection(dominationComparer, seed);
BiObjective.GeneticAlgorithm <bool> ga = new BiObjective.GeneticAlgorithm <bool>(evaluation, stopCondition, generator, selection, crossover, mutation, 100, seed);
ga.Run();
ReportBiObjectiveOptimizationResult(ga.Result);
}
static void Main(string[] args)
{
var random = new Random();
// Generate a list of products with random weights and prices
var products = new List <Product>();
for (var i = 0; i < 50; i++)
{
products.Add(new Product(random.Next(100) + 1, random.Next(100) + 1));
}
var maxWeight = 200;
var populationFactory = new BasicBinaryPopulationFactory(products.Count, false);
// Terminate if the highest fitness score remains the same after X generations
var termination = new StalenessTermination <BitArray>(5);
var fitnessEvaluator = new KnapsackFitnessEvaluator(maxWeight, products.AsReadOnly());
var crossover = new BinarySinglePointCrossover();
// Preserve the best chromosome for the next generation
var preservationSelection = new BestFitnessSelection <BitArray>(1);
// Use the top 30 chromosomes to create children for the next generation
var parentSelection = new BestFitnessSelection <BitArray>(30);
// Each gene has a 1% chance to mutate. Each mutation is a bit flip
var mutation = new BinaryBitFlipMutation(0.01);
var geneticAlgorithm = new GeneticAlgorithm <BitArray>(populationFactory, termination, fitnessEvaluator, preservationSelection, parentSelection, crossover, mutation, 200);
// Let the "breeding" begin
var geneticAlgorithmResult = geneticAlgorithm.Start();
var bestChromosome = geneticAlgorithmResult.Selection.First();
Console.WriteLine();
Console.WriteLine($"Max score using genetic algorithm: {bestChromosome.FitnessScore}");
Console.WriteLine($"Max score using dynamic programming: {CalculateMaximumValue(products.ToArray(), maxWeight)}");
}
private String Lab6BinaryIslandModel(IEvaluation <bool> evaluation, ASelection selection, ACrossover crossover, double pMut, int popSize, int islandCount, PopulationChoosingMethod method, int maxNCD, int?seed)
{
IterationsStopCondition stopCondition = new IterationsStopCondition(evaluation.dMaxValue, 100);
BinaryRandomGenerator generator = new BinaryRandomGenerator(evaluation.pcConstraint, seed);
BinaryBitFlipMutation mutation = new BinaryBitFlipMutation(pMut, evaluation, seed);
APopulationCrossover <bool> populationCrossover = new ParametrizedPopulationCrossover <bool>(crossover, .5, method);
IslandModel im = new IslandModel(evaluation, stopCondition, generator, selection, crossover,
mutation, popSize, islandCount, maxNCD, populationCrossover);
GAStallDetection <bool> ga = new GAStallDetection <bool>(evaluation, stopCondition, generator, selection, crossover, mutation, 20, 5);
ga.Run();
ReportOptimizationResult(ga.Result);
return(FormatSave(ga));
}
private List <String> AG(int seed, IEvaluation <bool>[] problems, ACrossover crossover)
{
List <String> results = new List <String>();
foreach (var evaluation in problems)
{
IterationsStopCondition stopCondition = new IterationsStopCondition(evaluation.dMaxValue, 1000);
BinaryRandomGenerator generator = new BinaryRandomGenerator(evaluation.pcConstraint, 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();
results.Add(FormatSave(ga));
}
return(results);
}
private List <String> AGDsmWithCrossover(int seed, IEvaluation <bool>[] problems)
{
List <String> results = new List <String>();
foreach (var evaluation in problems)
{
IterationsStopCondition stopCondition = new IterationsStopCondition(evaluation.dMaxValue, 1000);
BinaryRandomGenerator generator = new BinaryRandomGenerator(evaluation.pcConstraint, seed);
BinaryBitFlipMutation mutation = new BinaryBitFlipMutation(1.0 / evaluation.iSize, evaluation, seed);
TournamentSelection selection = new TournamentSelection(2, seed);
GAWithDSM dsmGAWithCross = new GAWithDSM(evaluation, stopCondition, generator, selection, mutation, 50, 0.5, 0.5);
dsmGAWithCross.Run();
results.Add(FormatSave(dsmGAWithCross));
}
return(results);
}
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);
}
private static Experiment <bool, Tuple <double, double>, BiObjective.OptimizationResult <bool> > PrepareExperiment9(int method, int evaluationType, int selectionMethod, double mutationProb, int populationSize, int?seed = null)
{
IEvaluation <bool, Tuple <double, double> > evaluation;
Dictionary <string, string> info = new Dictionary <string, string>();
DefaultDominationComparer dominationComparer = new DefaultDominationComparer();
switch (evaluationType)
{
case 0:
evaluation = new CBinaryZeroMaxOneMaxEvaluation(100);
info["problem"] = "ZeroMaxOneMax";
break;
case 1:
evaluation = new CBinaryTrapInvTrapEvaluation(5, 100);
info["problem"] = "Trap5InvTrap5";
break;
case 2:
evaluation = new CBinaryLOTZEvaluation(10);
info["problem"] = "LOTZ";
break;
case 3:
evaluation = new CBinaryMOMaxCutEvaluation(EBinaryBiObjectiveMaxCutInstance.maxcut_instance_100);
info["problem"] = "MaxCut";
break;
case 4:
evaluation = new CBinaryMOKnapsackEvaluation(EBinaryBiObjectiveKnapsackInstance.knapsack_100);
info["problem"] = "MOKnapsack";
break;
default:
throw new ArgumentOutOfRangeException();
}
info["variables"] = evaluation.iSize.ToString();
RunningTimeStopCondition stopCondition = new RunningTimeStopCondition(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);
ASelection <Tuple <double, double> > selection;
switch (selectionMethod)
{
case 0:
selection = new SampleBiObjectiveSelection(dominationComparer, seed);
info["selection_method"] = "Sample";
break;
case 1:
selection = new NSGA2Selection(10, evaluation.tMaxValue, dominationComparer, true, seed);
info["selection_method"] = "NSGA2";
break;
default:
throw new ArgumentOutOfRangeException();
}
mutationProb = mutationProb / evaluation.iSize;
info["mutation_prob"] = mutationProb.ToString();
info["population"] = populationSize.ToString();
BiObjective.GeneticAlgorithm <bool> ga;
switch (method)
{
case 0:
ga = new BiObjective.GeneticAlgorithm <bool>(evaluation, stopCondition, generator, selection, crossover, mutation, populationSize, seed);
info["method"] = "GA";
break;
case 1:
ga = new BiObjective.ClusteringGeneticAlgorithm <bool>(evaluation, stopCondition, generator, selection, crossover, mutation, populationSize, populationSize / 10, seed);
info["method"] = "ClusteringGA";
break;
case 2:
ga = new BiObjective.ClusteringGeneticAlgorithm2 <bool>(evaluation, stopCondition, generator, selection, crossover, mutation, populationSize, populationSize / 10, seed);
info["method"] = "ClusteringGA2";
break;
default:
throw new ArgumentOutOfRangeException();
}
Experiment <bool, Tuple <double, double>, BiObjective.OptimizationResult <bool> > experiment = new Experiment <bool, Tuple <double, double>, BiObjective.OptimizationResult <bool> >(ga, info, ExperimentFinished);
return(experiment);
}
private static Experiment <bool> PrepareExperiment6(int evaluationType, int algorithm, 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 CBinaryMax3SatEvaluation(100);
info["problem"] = "Max3Sat";
info["variables"] = "100";
break;
case 1:
evaluation = new CBinaryIsingSpinGlassEvaluation(100);
info["problem"] = "ISG";
info["variables"] = "100";
break;
case 2:
evaluation = new CBinaryNKLandscapesEvaluation(100);
info["problem"] = "NKLandscapes";
info["variables"] = "100";
break;
case 3:
evaluation = new CBinaryStandardDeceptiveConcatenationEvaluation(3, 10);
info["problem"] = "Deceptive";
info["variables"] = "10";
break;
case 4:
evaluation = new CBinaryStandardDeceptiveConcatenationEvaluation(3, 50);
info["problem"] = "Deceptive";
info["variables"] = "50";
break;
case 5:
evaluation = new CBinaryStandardDeceptiveConcatenationEvaluation(3, 100);
info["problem"] = "Deceptive";
info["variables"] = "100";
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 ResettingGA <bool>(evaluation, stopCondition, generator, selection, crossover, mutation, populationSize, 10, seed);
info["algorithm"] = "resetting";
break;
default:
throw new ArgumentOutOfRangeException();
}
Experiment <bool> experiment = new Experiment <bool>(ga, info, ExperimentFinished);
return(experiment);
}
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);
}
