public void TransportMutationTest1()
{
int populationSize = 2000;
file = root + "\\bays29.xml";
XDocument tspFile = XDocument.Load(file);
AdjacencyMatrix testMatrix = new AdjacencyMatrix(tspFile);
PMXCrossover crossover = new PMXCrossover((float)(0.80));
TournamentSelection selector = new TournamentSelection((int)populationSize / 2);
TranspositionMutation mutation = new TranspositionMutation((float)1);
GeneticSolver solver = new GeneticSolver(testMatrix, mutation, crossover, selector, populationSize, 10);
List <Candidate> listCand = solver.randomPopulation();
Candidate parentX = listCand[0];
Candidate parentY = listCand[1];
parentX.chromoson = new List <int>()
{
1, 2, 3, 4, 5, 6, 7, 8, 9
};
parentY.chromoson = new List <int>()
{
5, 3, 6, 7, 8, 1, 2, 9, 4,
};
mutation.Mutate(parentX);
}
static void Main(string[] args)
{
var selection = new TournamentSelection();
var crossover = new OrderBasedCrossover();
var mutation = new ReverseSequenceMutation();
var fitness = new SortFitness();
var chromosomes = new SortChromosomes();
var population = new Population(40, 40, chromosomes);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new GenerationNumberTermination(100);
Console.WriteLine("GA running...");
ga.Start();
Console.WriteLine("GA done in {0} generations.", ga.GenerationsNumber);
var bestChromosome = ga.BestChromosome as SortChromosomes;
Console.WriteLine("Best solution found");
DisplayChromosome(bestChromosome);
Console.ReadKey();
}
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);
}
public Tuple <List <String>, List <String> > Lab6FindStallingParameter(IEvaluation <bool>[] problems, int[] seeds)
{
List <String> stuckResults = new List <String>();
List <String> results = new List <String>();
double[] pMuts = new double[] { 0.01, 0.001, 0.0001, 0.00000001 };
int[] popSizes = new int[] { 100, 50, 20, 4 };
int iter = 1;
int maxI = pMuts.Length * popSizes.Length;
foreach (var pMut in pMuts)
{
foreach (var popSize in popSizes)
{
Dictionary <String, int> problemStallCounter = new Dictionary <String, int>();
foreach (var seed in seeds)
{
TournamentSelection selection = new TournamentSelection(4, seed);
OnePointCrossover crossover = new OnePointCrossover(.5);
foreach (var problem in problems)
{
results.Add(Lab6BinaryGA(problem, selection, crossover, pMut, popSize, problemStallCounter, seed));
}
}
Console.WriteLine("\nParameters - mutation probability: " + pMut.ToString() + ", population size: " +
popSize.ToString() + " /// " + DateTime.Now.ToString("HH:mm:ss.fff") + " | " + iter.ToString() + "/" + maxI.ToString());
stuckResults.Add(pMut.ToString() + ", " + popSize.ToString() + ", " + String.Join(", ", problemStallCounter.Select(x => x.Value).ToArray()));
problemStallCounter.Select(i => $"{i.Key}: {i.Value}").ToList().ForEach(Console.WriteLine);
iter++;
}
}
return(new Tuple <List <String>, List <String> >(results, stuckResults));
}
public ISelection Tournament()
{
var target = new TournamentSelection();
target.SelectChromosomes(_chromosomesNumber, _generation);
return(target);
}
public void Selection_TournamentSizeSameAsPopulation_TwoBestFittedChromosomes()
{
var population = new List <IChromosome <decimal> >();
var chromosome1 = new Chromosome <decimal>(new IGene <decimal>[] { new Gene <decimal>(new [] { 1.2m, 1.3m }, 0.1m),
new Gene <decimal>(new [] { 1.3m, 1.5m }, 0.1m) });
chromosome1.FintnessValue = 90;
var chromosome2 = new Chromosome <decimal>(new IGene <decimal>[] { new Gene <decimal>(new [] { 2.2m, 2.3m }, 0.1m),
new Gene <decimal>(new [] { 2.3m, 2.5m }, 0.1m) });
chromosome2.FintnessValue = 80;
var chromosome3 = new Chromosome <decimal>(new IGene <decimal>[] { new Gene <decimal>(new [] { 3.2m, 3.3m }, 0.1m),
new Gene <decimal>(new [] { 3.3m, 3.5m }, 0.1m) });
chromosome3.FintnessValue = 70;
population.Add(chromosome1);
population.Add(chromosome2);
population.Add(chromosome3);
var selector = new TournamentSelection(3);
var result = selector.Selection(population);
Assert.IsTrue(result.Item1.FintnessValue == 90 || result.Item2.FintnessValue == 90);
}
public void OXTest1()
{
int populationSize = 120;
file = root + "\\bays29.xml";
XDocument tspFile = XDocument.Load(file);
AdjacencyMatrix testMatrix = new AdjacencyMatrix(tspFile);
OXCrossover crossover = new OXCrossover((float)(1));
TournamentSelection selector = new TournamentSelection(5);
InversionMutation inv = new InversionMutation((float)0.05);
GeneticSolver solver = new GeneticSolver(testMatrix, inv, crossover, selector, populationSize, 10);
List <Candidate> listCand = solver.randomPopulation();
Candidate parentX = listCand[0];
Candidate parentY = listCand[1];
parentX.chromoson = new List <int>()
{
1, 2, 3, 4, 5, 6, 7, 8, 9
};
parentY.chromoson = new List <int>()
{
5, 3, 6, 7, 8, 1, 2, 9, 4,
};
crossover.Crossover(parentX, parentY);
}
private void button1_Click(object sender, EventArgs e)
{
Gen.length = Convert.ToInt32(RangGen.Value);
double[] Xmin = new double[Convert.ToInt32(NGen.Value)];
double[] Xmax = new double[Convert.ToInt32(NGen.Value)];
for (int i = 0; i < Convert.ToInt32(NGen.Value); i++)
{
Xmin[i] = Convert.ToDouble(xmin.Text);
Xmax[i] = Convert.ToDouble(xmax.Text);
}
Generator generator = new Generator(Xmin, Xmax, Convert.ToInt32(Individ.Value), Convert.ToInt32(NGen.Value)); //генерируем исходные данные
individuals = generator.generation(); //сгенерировали особей
List <double> temp = new List <double>();
for (int i = 0; i < Convert.ToInt32(generation.Value); i++)
{
temp.Add(individuals.Min(f => f.fitness));
Selection selection = new TournamentSelection(Convert.ToInt32(sizeTurnir.Value), 1); //турнирный отбор в селекции
individuals = selection.selectionRun(individuals); //оценили приспособленность и отобрали для скрещивания (кроссовера)
Crossovers Crosover = new IntOnePointCrossover(1, Convert.ToDouble(Pc.Value));
individuals = Crosover.crossoverRun(individuals); //Запуск кроссовера(скрещиваниe)
individuals.Sort();
}
}
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 void PMXTest2()
{
int populationSize = 120;
file = root + "\\bays29.xml";
XDocument tspFile = XDocument.Load(file);
AdjacencyMatrix testMatrix = new AdjacencyMatrix(tspFile);
PMXCrossover crossover = new PMXCrossover((float)(0.80));
TournamentSelection selector = new TournamentSelection(5);
InversionMutation inv = new InversionMutation((float)0.05);
GeneticSolver solver = new GeneticSolver(testMatrix, inv, crossover, selector, populationSize, 10);
List <Candidate> listCand = new List <Candidate>();
List <int> GenX = new List <int>()
{
5, 20, 28, 18, 14, 2, 27, 25, 8, 4, 19, 13, 12, 17, 11, 15, 16, 9, 3, 10, 22, 21, 1, 7, 24, 6, 23, 26
};
List <int> GenY = new List <int>()
{
28, 19, 11, 27, 3, 18, 17, 14, 10, 23, 8, 12, 6, 2, 22, 7, 25, 20, 4, 1, 26, 9, 5, 15, 24, 21, 16, 13
};
Candidate parentX = new Candidate(1, GenX, solver, solver.time.ElapsedMilliseconds.ToString());
Candidate parentY = new Candidate(1, GenY, solver, solver.time.ElapsedMilliseconds.ToString());
listCand.Add(parentX);
listCand.Add(parentY);
crossover.CrossoverPopulation(listCand, 10);
}
public static void createNewSolver(int mutationIndex, int crossoverIndex, int selectorIndex, int populationSize, float mutationChance, int timeMS, int selectorSize, float crossoverChance)
{
MutationType mutation = null;
CrossoverType crossover = null;
SelectionType selection = null;
AdjacencyMatrix matrix = new AdjacencyMatrix(tspXmlFile);
switch (mutationIndex)
{
case 0:
{
mutation = new InversionMutation(mutationChance);
break;
}
case 1:
{
mutation = new TranspositionMutation(mutationChance);
break;
}
}
switch (crossoverIndex)
{
case 0:
{
crossover = new PMXCrossover(crossoverChance);
break;
}
case 1:
{
crossover = new OXCrossover(crossoverChance);
break;
}
}
switch (selectorIndex)
{
case 0:
{
selection = new TournamentSelection(selectorSize);
break;
}
case 1:
{
selection = new RouletteSelection(selectorSize);
break;
}
}
GeneticSolver solver = null;//add parameters TO DO
if (mutation != null && selection != null && crossover != null)
{
addNewSolver(new GeneticSolver(matrix, mutation, crossover, selection, populationSize, timeMS));
}
}
public void ItCanChooseParentsForTournamentFive()
{
tournament = new TournamentFiveSelection();
tournament.Setup(GetStepChromosomes(), _config);
var parents = tournament.GetParents();
Assert.IsNotNull(parents);
}
public void SelectChromosomes_NullGeneration_Exception()
{
var target = new TournamentSelection();
ExceptionAssert.IsThrowing(new ArgumentNullException("generation"), () =>
{
target.SelectChromosomes(2, null);
});
}
public void SelectChromosomes_TournamentSize3AllowWinnerCompeteNextTournamentFalse_ChromosomesSelected()
{
var target = new TournamentSelection(3, false);
var c0 = MockRepository.GeneratePartialMock <ChromosomeBase>(2);
c0.Fitness = 0.1;
var c1 = MockRepository.GeneratePartialMock <ChromosomeBase>(2);
c1.Fitness = 0.5;
var c2 = MockRepository.GeneratePartialMock <ChromosomeBase>(2);
c2.Fitness = 0;
var c3 = MockRepository.GeneratePartialMock <ChromosomeBase>(2);
c3.Fitness = 0.7;
var c4 = MockRepository.GeneratePartialMock <ChromosomeBase>(2);
c4.Fitness = 0.3;
var c5 = MockRepository.GeneratePartialMock <ChromosomeBase>(2);
c5.Fitness = 0.2;
var generation = new Generation(1, new List <IChromosome>()
{
c0, c1, c2, c3, c4, c5
});
var mock = new MockRepository();
var rnd = mock.StrictMock <IRandomization>();
using (mock.Ordered())
{
rnd.Expect(r => r.GetUniqueInts(3, 0, 6)).Return(new int[] { 0, 1, 2 });
rnd.Expect(r => r.GetUniqueInts(3, 0, 5)).Return(new int[] { 2, 3, 4 });
rnd.Expect(r => r.GetUniqueInts(3, 0, 4)).Return(new int[] { 0, 1, 2 });
rnd.Expect(r => r.GetUniqueInts(3, 0, 3)).Return(new int[] { 0, 1, 2 });
}
RandomizationProvider.Current = rnd;
mock.ReplayAll();
var actual = target.SelectChromosomes(4, generation);
Assert.AreEqual(4, actual.Count);
Assert.AreEqual(c1, actual[0]);
Assert.AreEqual(c3, actual[1]);
Assert.AreEqual(c4, actual[2]);
Assert.AreEqual(c5, actual[3]);
}
public void SelectChromosomes_NullGeneration_Exception()
{
var target = new TournamentSelection();
var actual = Assert.Catch <ArgumentNullException>(() =>
{
target.SelectChromosomes(2, null);
});
Assert.AreEqual("generation", actual.ParamName);
}
private static IMigrationStrategy CreateMigrationStrategy(IslandEngine engine, IRandom random, IFitnessComparer fitnessComparer, ParameterSet parameters, PeaSettings settings)
{
//TODO: MigrationFactory!
var selection = new TournamentSelection(random, fitnessComparer, parameters);
var replacement = new ReplaceWorstEntitiesOfPopulation(random, fitnessComparer, parameters);
var strategy = new Migration.Implementation.MigrationStrategy(random, selection, replacement, engine.Parameters);
strategy.Parameters.SetValue(Migration.ParameterNames.MigrationReceptionRate, 0.01);
return(strategy);
}
public override void InitPopulation()
{
//You should implement the code to initialize the population here
population = new List <Individual> ();
// jncor
selection = new TournamentSelection();
while (population.Count < populationSize)
{
GeneticIndividual new_ind = new GeneticIndividual(topology, crossoverPoints);
new_ind.Initialize();
population.Add(new_ind);
}
}
public void SelectChromosomes_TournamentSize3AllowWinnerCompeteNextTournamentTrue_ChromosomesSelected()
{
var target = new TournamentSelection(3, true);
var c0 = Substitute.ForPartsOf <ChromosomeBase>(2);
c0.Fitness = 0.1;
var c1 = Substitute.ForPartsOf <ChromosomeBase>(2);
c1.Fitness = 0.5;
var c2 = Substitute.ForPartsOf <ChromosomeBase>(2);
c2.Fitness = 0;
var c3 = Substitute.ForPartsOf <ChromosomeBase>(2);
c3.Fitness = 0.7;
var c4 = Substitute.ForPartsOf <ChromosomeBase>(2);
c4.Fitness = 0.3;
var c5 = Substitute.ForPartsOf <ChromosomeBase>(2);
c5.Fitness = 0.2;
var generation = new Generation(1, new List <IChromosome>()
{
c0, c1, c2, c3, c4, c5
});
var rnd = Substitute.For <IRandomization>();
rnd.GetUniqueInts(3, 0, 6).Returns(
new int[] { 0, 1, 2 },
new int[] { 3, 4, 5 },
new int[] { 0, 2, 4 },
new int[] { 1, 3, 5 });
RandomizationProvider.Current = rnd;
var actual = target.SelectChromosomes(4, generation);
Assert.AreEqual(4, actual.Count);
Assert.AreEqual(c1, actual[0]);
Assert.AreEqual(c3, actual[1]);
Assert.AreEqual(c4, actual[2]);
Assert.AreEqual(c3, actual[3]);
}
public IPredictionResult Predict <TDistribution>(IHiddenMarkovModel <TDistribution> model, IPredictionRequest request) where TDistribution : IDistribution
{
var selectionMethod = new TournamentSelection(TournamentSize);
var crossoverAlgorithm = new Crossover(CrossoverProbability);
var mutationAlgorithm = new Mutator(MutationProbability);
var evaluator = new HmmEvaluator <TDistribution>(model, new ForwardBackward(true));
var parameters = new GeneticSolverParameters
{
CrossOverProbability = CrossoverProbability,
MutationProbability = MutationProbability,
NumberOfGenerations = NumberOfGenerations,
PopulationSize = request.NumberOfDays * 10,
TournamentSize = TournamentSize
};
var predictions = new PredictionResult {
Predicted = new double[request.NumberOfDays][]
};
var solver = new GeneticSolver(parameters, mutationAlgorithm, crossoverAlgorithm, evaluator, selectionMethod);
var populationInitializer = new FromTimeSeriesRandomInitializer(request.TrainingSet);
var population = populationInitializer.Initialize <decimal>(parameters.PopulationSize, request.NumberOfDays, MutationProbability);
for (int i = 0; i < population.Count; i++)
{
var chromosome = population[i];
population[i].FintnessValue = evaluator.Evaluate(chromosome);
}
var result = solver.Solve(population);
// Get best fitted chromosome
var maximumFitness = result[0].FintnessValue;
var solution = result[0];
foreach (var chromosome in result)
{
if (maximumFitness <= chromosome.FintnessValue)
{
solution = chromosome;
maximumFitness = chromosome.FintnessValue;
}
}
// Convert it to array
for (int i = 0; i < solution.Representation.Length; i++)
{
predictions.Predicted[i] = Array.ConvertAll(solution.Representation[i].Representation, x => (double)Convert.ChangeType(x, typeof(double)));
}
return(predictions);
}
public void TournamentTest1()
{
int populationSize = 120;
file = root + "\\bays29.xml";
XDocument tspFile = XDocument.Load(file);
AdjacencyMatrix testMatrix = new AdjacencyMatrix(tspFile);
PMXCrossover crossover = new PMXCrossover((float)(0.80));
TournamentSelection selector = new TournamentSelection(10);
InversionMutation inv = new InversionMutation((float)0.05);
GeneticSolver solver = new GeneticSolver(testMatrix, inv, crossover, selector, populationSize, 10);
// listCand = selector.generateBreedingPool(listCand);
}
public override void InitPopulation()
{
maxNumberOfEvaluations = Mathf.Min(maxNumberOfEvaluations, populationSize);
populationRed = new List <Individual>();
populationBlue = new List <Individual>();
while (populationRed.Count < populationSize)
{
GeneticIndividual new_ind_red = new GeneticIndividual(NNTopology, maxNumberOfEvaluations, mutationMethod)
{
Mean = Mean,
Stdev = Stdev
};
GeneticIndividual new_ind_blue = new GeneticIndividual(NNTopology, maxNumberOfEvaluations, mutationMethod)
{
Mean = Mean,
Stdev = Stdev
};
if (seedPopulationFromFile)
{
NeuralNetwork nnRed = getRedIndividualFromFile();
NeuralNetwork nnBlue = getBlueIndividualFromFile();
new_ind_red.Initialize(nnRed);
new_ind_blue.Initialize(nnBlue);
//only the first individual is an exact copy. the other are going to suffer mutations
if (populationRed.Count != 0 && populationBlue.Count != 0)
{
new_ind_red.Mutate(mutationProbabilityRedPopulation);
new_ind_blue.Mutate(mutationProbabilityBluePopulation);
}
}
else
{
new_ind_red.Initialize();
new_ind_blue.Initialize();
}
populationRed.Add(new_ind_red);
populationBlue.Add(new_ind_blue);
}
switch (selectionMethod)
{
case SelectionType.Tournament:
selection = new TournamentSelection(tournamentSize);
break;
}
}
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 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);
}
public static AbstractSelection ChosenSelectionMethod(SelectionMethod selectionMethod, int countTour, int countSelect)
{
AbstractSelection selection = null;
switch (selectionMethod)
{
case SelectionMethod.Tournament:
selection = new TournamentSelection(countSelect, countTour);
break;
case SelectionMethod.Proportion:
selection = new ProportionSelection(countSelect);
break;
}
return(selection);
}
private GeneticSolver getSolver()
{
int populationSize = 3000;
file = root + "\\rbg403.xml";
XDocument tspFile = XDocument.Load(file);
AdjacencyMatrix testMatrix = new AdjacencyMatrix(tspFile);
PMXCrossover crossover = new PMXCrossover((float)(0.80));
TournamentSelection selector = new TournamentSelection((int)(5));
InversionMutation inv = new InversionMutation((float)0.05);
GeneticSolver solver = new GeneticSolver(
testMatrix, inv, crossover, selector, populationSize, 60);
return(solver);
}
/// <summary>
/// Construct an EA.
/// </summary>
/// <param name="thePopulation">The population.</param>
/// <param name="theScoreFunction">The score function.</param>
public BasicEA(IPopulation thePopulation,
ICalculateScore theScoreFunction)
{
RandomNumberFactory = EncogFramework.Instance.RandomFactory.FactorFactory();
EliteRate = 0.3;
MaxTries = 5;
MaxOperationErrors = 500;
CODEC = new GenomeAsPhenomeCODEC();
Population = thePopulation;
ScoreFunction = theScoreFunction;
Selection = new TournamentSelection(this, 4);
Rules = new BasicRuleHolder();
// set the score compare method
if (theScoreFunction.ShouldMinimize)
{
SelectionComparer = new MinimizeAdjustedScoreComp();
BestComparer = new MinimizeScoreComp();
}
else
{
SelectionComparer = new MaximizeAdjustedScoreComp();
BestComparer = new MaximizeScoreComp();
}
// set the iteration
foreach (ISpecies species in thePopulation.Species)
{
foreach (IGenome genome in species.Members)
{
IterationNumber = Math.Max(IterationNumber,
genome.BirthGeneration);
}
}
// Set a best genome, just so it is not null.
// We won't know the true best genome until the first iteration.
if (Population.Species.Count > 0 && Population.Species[0].Members.Count > 0)
{
BestGenome = Population.Species[0].Members[0];
}
}
private static void TestCrossoverVsMutation()
{
List <double> b = new List <double>();
List <double> w = new List <double>();
List <double> a = new List <double>();
var evaluation = new TspEvaluation(@"C:\Users\jbelter\source\repos\machine-learning-cvrp\data\A-n46-k7.vrp");
double[] px = { 0, .1, .8, 1, 0, 0, 0 };
double[] pw = { 0, 0, 0, 0, .2, .5, 1 };
for (int j = 0; j < 7; j++)
{
for (int i = 0; i < 20; i++)
{
var stopCondition = new IterationsStopCondition(200);
//var optimizer = new TspRandomSearch(evaluation, stopCondition);
var generator = new TspGenerator(new Random());
ASelection selection = new TournamentSelection(Convert.ToInt32(5));
var crossover = new OrderedCrossover(px[j]);
var mutation = new SwapMutation(pw[j]);
var optimizer = new GeneticAlgorithm <int>(evaluation, stopCondition, generator, selection, crossover, mutation, 100);
optimizer.Run();
b.Add(optimizer.bestSolutions.Last());
w.Add(optimizer.worstValues.Last());
a.Add(optimizer.averageValues.Last());
}
double avgB = 0.0, avgA = 0.0, avgW = 0.0;
for (int i = 0; i < b.Count; i++)
{
avgB += b[i];
avgA += a[i];
avgW += w[i];
}
avgB /= b.Count;
avgA /= b.Count;
avgW /= b.Count;
Console.WriteLine("avg(best value): " + avgB.ToString() +
" avg(average value): " + avgA.ToString() +
" avg(worst value): " + avgW.ToString());
}
}
public void Selection_TournamentSize5AndPopulationSize10_BestFittedChromosomes()
{
var population = new List <IChromosome <decimal> >();
for (int i = 0; i < 10; i++)
{
var chromosome = new Chromosome <decimal>(new IGene <decimal>[] { new Gene <decimal>(new [] { i + .2m, i + .3m }, 0.1m),
new Gene <decimal>(new [] { i + .3m, i + .5m }, 0.1m) });
chromosome.FintnessValue = 5 * i;
population.Add(chromosome);
}
var selector = new TournamentSelection(5);
var selected = selector.Selection(population);
Assert.IsTrue(selected.Item1.FintnessValue > 30 || selected.Item2.FintnessValue > 30);
}
public void solverTest()
{
int populationSize = 3000;
file = root + "\\rbg403.xml";
XDocument tspFile = XDocument.Load(file);
AdjacencyMatrix testMatrix = new AdjacencyMatrix(tspFile);
PMXCrossover crossover = new PMXCrossover((float)(0.80));
TournamentSelection selector = new TournamentSelection((int)(5));
InversionMutation inv = new InversionMutation((float)0.05);
GeneticSolver solver = new GeneticSolver(
testMatrix, inv, crossover, selector, populationSize, 480);
var result = solver.Solve();
result.resultToXML();
result.ToFile();
}
public void SelectChromosomes_InvalidNumber_Exception()
{
var target = new TournamentSelection();
ExceptionAssert.IsThrowing(new ArgumentOutOfRangeException("number", "The number of selected chromosomes should be at least 2."), () =>
{
target.SelectChromosomes(-1, null);
});
ExceptionAssert.IsThrowing(new ArgumentOutOfRangeException("number", "The number of selected chromosomes should be at least 2."), () =>
{
target.SelectChromosomes(0, null);
});
ExceptionAssert.IsThrowing(new ArgumentOutOfRangeException("number", "The number of selected chromosomes should be at least 2."), () =>
{
target.SelectChromosomes(1, null);
});
}
static void Main(string[] args)
{
var teams = GetTeams();
var generator = new FixtureGenerator(teams, 2);
var seasons = new List<Season>();
for (int i = 0; i < NumberOfSeasons; i++)
{
seasons.Add(generator.GenerateFixtures());
}
var criteria = GetCriteria();
var selectionStrategy = new TournamentSelection<Season, MatchDay>(0.8, criteria);
var seasonsCrossedOver = new List<Season>(seasons);
for (int i = 0; i < NumberOfGenerations; i++)
{
var simpleCrossover = new SimpleCrossoverStrategy<Season, MatchDay>();
var populationGenerator = new PopulationGenerator<Season, MatchDay>(seasonsCrossedOver, NumberOfChildrenPerGeneration, simpleCrossover, selectionStrategy);
seasonsCrossedOver = populationGenerator.Generate();
string statistics = populationGenerator.GetStatistics(criteria);
Console.WriteLine("Statistics for generation " + (i + 1));
Console.WriteLine(statistics);
}
var bestSeason = seasonsCrossedOver.OrderByDescending(season => new CriteriaCalculator<Season, MatchDay>(season, criteria).Calculate().Score).FirstOrDefault();
DisplayFixtures(bestSeason);
var calculator = new CriteriaCalculator<Season, MatchDay>(bestSeason, criteria);
var result = calculator.Calculate();
DisplayResults(result);
Console.ReadLine();
}