public void Mutate_NoProbality_NoInsertion()
{
var target = new InsertionMutation();
var chromosome = Substitute.For <ChromosomeBase>(4);
chromosome.ReplaceGenes(0, new Gene[]
{
new Gene(1),
new Gene(2),
new Gene(3),
new Gene(4),
});
var rnd = Substitute.For <IRandomization>();
rnd.GetDouble().Returns(0.1);
RandomizationProvider.Current = rnd;
target.Mutate(chromosome, 0);
Assert.AreEqual(4, chromosome.Length);
Assert.AreEqual(1, chromosome.GetGene(0).Value);
Assert.AreEqual(2, chromosome.GetGene(1).Value);
Assert.AreEqual(3, chromosome.GetGene(2).Value);
Assert.AreEqual(4, chromosome.GetGene(3).Value);
}
public void Mutate_ValidChromosome_Insertion_To_Left()
{
var target = new InsertionMutation();
var chromosome = Substitute.For <ChromosomeBase>(8);
chromosome.ReplaceGenes(0, new Gene[]
{
new Gene(1),
new Gene(2),
new Gene(3),
new Gene(4),
new Gene(5),
new Gene(6),
new Gene(7),
new Gene(8),
});
var rnd = Substitute.For <IRandomization>();
rnd.GetUniqueInts(2, 0, 8).Returns(new int[] { 1, 6 });
rnd.GetDouble().Returns(0);
RandomizationProvider.Current = rnd;
target.Mutate(chromosome, 1);
Assert.AreEqual(8, chromosome.Length);
Assert.AreEqual(1, chromosome.GetGene(0).Value);
Assert.AreEqual(3, chromosome.GetGene(1).Value);
Assert.AreEqual(4, chromosome.GetGene(2).Value);
Assert.AreEqual(5, chromosome.GetGene(3).Value);
Assert.AreEqual(6, chromosome.GetGene(4).Value);
Assert.AreEqual(7, chromosome.GetGene(5).Value);
Assert.AreEqual(2, chromosome.GetGene(6).Value);
Assert.AreEqual(8, chromosome.GetGene(7).Value);
}
public void Mutate_NoProbality_NoInsertion()
{
var target = new InsertionMutation();
var chromosome = MockRepository.GenerateStub <ChromosomeBase>(4);
chromosome.ReplaceGenes(0, new Gene[]
{
new Gene(1),
new Gene(2),
new Gene(3),
new Gene(4),
});
var rnd = MockRepository.GenerateMock <IRandomization>();
rnd.Expect(r => r.GetDouble()).Return(0.1);
RandomizationProvider.Current = rnd;
target.Mutate(chromosome, 0);
Assert.AreEqual(4, chromosome.Length);
Assert.AreEqual(1, chromosome.GetGene(0).Value);
Assert.AreEqual(2, chromosome.GetGene(1).Value);
Assert.AreEqual(3, chromosome.GetGene(2).Value);
Assert.AreEqual(4, chromosome.GetGene(3).Value);
rnd.VerifyAllExpectations();
chromosome.VerifyAllExpectations();
}
public IMutation InsertionMutation()
{
var target = new InsertionMutation();
target.Mutate(new TspChromosome(_numberOfCities), _probability);
return(target);
}
public void InsertionMutationShouldCreateANewRoute()
{
var settings = new GASettings();
var random = A.Fake <IRandom>();
var map = A.Dummy <Roteiro>();
var locals = GetLocals(6);
const int
indexTruck = 0,
indexLocalFrom = 1,
indexLocalTo = 0;
A.CallTo(() => random.NextDouble())
.ReturnsNextFromSequence(0, 0);
A.CallTo(() => random.Next(A <int> ._, A <int> ._))
.ReturnsNextFromSequence(
indexTruck,
indexLocalFrom,
indexLocalTo
);
var gen = new Genome(map, settings)
{
Trucks = new[] {
new Truck {
Locals = locals.Take(3).ToArray()
},
new Truck {
Locals = locals.Skip(3).ToArray()
},
new Truck {
Locals = Enumerable.Empty <Local>().ToArray()
}
}
};
var mutate = new InsertionMutation(settings, random);
var newGen = mutate.Apply(gen);
var expectLocals = newGen.Trucks[2].Locals;
A.CallTo(() => random.Next(0, 0)).MustHaveHappenedOnceExactly();
newGen.Trucks[0].Locals.Should().HaveCount(2);
newGen.Trucks[0].Locals.Should().NotContain(locals[1]);
newGen.Trucks[1].Locals.Should().HaveCount(3);
expectLocals.Should().ContainSingle();
expectLocals.Should().Contain(locals[1]);
}
public void Mutate_LessThanThreeGenes_Exception()
{
var target = new InsertionMutation();
var chromosome = Substitute.For <ChromosomeBase>(2);
chromosome.ReplaceGenes(0, new Gene[]
{
new Gene(1),
});
Assert.Catch <MutationException>(() =>
{
target.Mutate(chromosome, 0);
}, "A chromosome should have, at least, 3 genes. {0} has only 2 gene.".With(chromosome.GetType().Name));
}
public void Mutate_LessThanThreeGenes_Exception()
{
var target = new InsertionMutation();
var chromosome = MockRepository.GenerateStub <ChromosomeBase>(2);
chromosome.ReplaceGenes(0, new Gene[]
{
new Gene(1),
});
ExceptionAssert.IsThrowing(new MutationException(target, "A chromosome should have, at least, 3 genes. {0} has only 2 gene.".With(chromosome.GetType().Name)), () =>
{
target.Mutate(chromosome, 0);
});
}
public void InsertionMutationTest3()
{
Organism organism = new Organism();
organism.Chromosomes.Add(new Chromosome(1, "10"));
IRandom rand = new Deterministic(0, 1);
InsertionMutation mutator = new InsertionMutation(rand);
mutator.Mutate(organism);
string answer = organism.Chromosomes[0].ToString();
Assert.AreEqual("110", answer);
}
public void InsertionMutationShouldNotCreateANewRoute()
{
var settings = new GASettings();
var random = A.Fake <IRandom>();
var map = A.Dummy <Roteiro>();
var locals = GetLocals(6);
const int
indexTruck = 0,
indexTruckDestination = 1,
indexLocalFrom = 1,
indexLocalTo = 2;
A.CallTo(() => random.NextDouble())
.ReturnsNextFromSequence(0, 1);
A.CallTo(() => random.Next(A <int> ._, A <int> ._))
.ReturnsNextFromSequence(
indexTruck,
indexTruckDestination,
indexLocalFrom,
indexLocalTo
);
var gen = new Genome(map, settings)
{
Trucks = new[] {
new Truck {
Locals = locals.Take(3).ToArray()
},
new Truck {
Locals = locals.Skip(3).ToArray()
},
new Truck {
Locals = Enumerable.Empty <Local>().ToArray()
}
}
};
var mutate = new InsertionMutation(settings, random);
var newGen = mutate.Apply(gen);
newGen.Trucks[indexTruck].Locals.Should().HaveCount(2);
newGen.Trucks[indexTruckDestination].Locals.Should().HaveCount(4);
newGen.Trucks[indexTruckDestination].Locals[indexLocalTo].Should().Be(locals[1]);
newGen.Trucks[2].Locals.Should().BeEmpty();
}
public void Mutate_ValidChromosome_Insertion_To_Left()
{
var target = new InsertionMutation();
var chromosome = MockRepository.GenerateStub <ChromosomeBase>(8);
chromosome.ReplaceGenes(0, new Gene[]
{
new Gene(1),
new Gene(2),
new Gene(3),
new Gene(4),
new Gene(5),
new Gene(6),
new Gene(7),
new Gene(8),
});
var rnd = MockRepository.GenerateMock <IRandomization>();
rnd.Expect(r => r.GetUniqueInts(2, 0, 8)).Return(new int[] { 1, 6 });
rnd.Expect(r => r.GetDouble()).Return(0);
RandomizationProvider.Current = rnd;
target.Mutate(chromosome, 1);
Assert.AreEqual(8, chromosome.Length);
Assert.AreEqual(1, chromosome.GetGene(0).Value);
Assert.AreEqual(3, chromosome.GetGene(1).Value);
Assert.AreEqual(4, chromosome.GetGene(2).Value);
Assert.AreEqual(5, chromosome.GetGene(3).Value);
Assert.AreEqual(6, chromosome.GetGene(4).Value);
Assert.AreEqual(7, chromosome.GetGene(5).Value);
Assert.AreEqual(2, chromosome.GetGene(6).Value);
Assert.AreEqual(8, chromosome.GetGene(7).Value);
rnd.VerifyAllExpectations();
chromosome.VerifyAllExpectations();
}
private static IAlgoritmo CriaAlgoritmoGenetico(Dictionary <string, string[]> dict, List <string> flat, Problema problema)
{
int populacaoMin, populacaoMax;
IPopulation population;
ISelection selection;
ICrossover crossover;
IMutation mutation;
ITermination termination;
IReinsertion reinsertion;
float crossoverProbability, mutationProbability;
var p = dict.ValueOrDefault("p", "50,100").Split(new[] { ',' });
if (p.Length != 2 || !int.TryParse(p[0], out populacaoMin) || !int.TryParse(p[1], out populacaoMax))
{
throw new ArgumentException("Faixa de população inválida.");
}
population = new Population(populacaoMin, populacaoMax, new CromossomoViajante(problema.Mapa.Locais.Count));
switch (dict.ValueOrDefault("s", "t"))
{
case "e":
selection = new EliteSelection();
break;
case "r":
selection = new RouletteWheelSelection();
break;
case "s":
selection = new StochasticUniversalSamplingSelection();
break;
case "t":
selection = new TournamentSelection();
break;
default:
throw new ArgumentException("Seleção inválida.");
}
switch (dict.ValueOrDefault("c", "o"))
{
case "s":
crossover = new CutAndSpliceCrossover();
break;
case "c":
crossover = new CycleCrossover();
break;
case "o":
crossover = new OrderedCrossover();
break;
case "ob":
crossover = new OrderBasedCrossover();
break;
case "op":
crossover = new OnePointCrossover();
break;
case "pm":
crossover = new PartiallyMappedCrossover();
break;
case "p":
crossover = new PositionBasedCrossover();
break;
case "tpa":
crossover = new ThreeParentCrossover();
break;
case "tp":
crossover = new TwoPointCrossover();
break;
case "u":
crossover = new UniformCrossover();
break;
default:
throw new ArgumentException("Crossover inválido.");
}
switch (dict.ValueOrDefault("m", "r"))
{
case "d":
mutation = new DisplacementMutation();
break;
case "f":
mutation = new FlipBitMutation();
break;
case "i":
mutation = new InsertionMutation();
break;
case "s":
mutation = new PartialShuffleMutation();
break;
case "r":
mutation = new ReverseSequenceMutation();
break;
case "t":
mutation = new TworsMutation();
break;
case "u":
mutation = new UniformMutation();
break;
default:
throw new ArgumentException("Mutação inválida.");
}
switch (dict.ValueOrDefault("t", "s"))
{
case "s":
termination = new FitnessStagnationTermination();
break;
case "t":
termination = new FitnessThresholdTermination();
break;
case "g":
termination = new GenerationNumberTermination();
break;
default:
throw new ArgumentException("Terminação inválida.");
}
switch (dict.ValueOrDefault("e", "e"))
{
case "e":
reinsertion = new ElitistReinsertion();
break;
case "p":
reinsertion = new PureReinsertion();
break;
case "u":
reinsertion = new UniformReinsertion();
break;
default:
throw new ArgumentException("Reinserção inválida.");
}
if (!float.TryParse(dict.ValueOrDefault("cp", "0,75"), out crossoverProbability))
{
throw new ArgumentException("Probabilidade de crossover inválida.");
}
if (!float.TryParse(dict.ValueOrDefault("mp", "0,25"), out mutationProbability))
{
throw new ArgumentException("Probabilidade de mutação inválida.");
}
return(new AlgoritmoGenetico(problema, population, selection, crossover, crossoverProbability, mutation, mutationProbability, termination, reinsertion));
}
static void Main(string[] args)
{
int populationSize = 10;
int numberOfEpoch = 50;
int numberOfExecution = 1;
double probabilityOfMutation = 0.1;
string file = @"WesternSahara.txt";
Cities testCities = new Cities(file);
IParentSelection parentSelection = new TournamentSelection();
ICrossover crossover = new PMX();
IMutation mutation = new InsertionMutation(probabilityOfMutation);
string filename = DateTime.Now.ToString("yyyy-MM-dd HH;mm");
//Test 1 - PMX
TSPAlgorithm tsp = new TSPAlgorithm(numberOfEpoch, populationSize, testCities, parentSelection, mutation, crossover);
AlgorithmExecutor AE = new AlgorithmExecutor(tsp, numberOfExecution);
AE.Start();
AE.SaveInformations(filename + ".txt");
//AE.ExportStatisticsToCsv(filename + "_stats.csv");
/* Test 2 - CX
* crossover = new CX();
* tsp = new TSPAlgorithm(numberOfEpoch, populationSize, testCities, parentSelection, mutation, crossover);
* AE = new AlgorithmExecutor(tsp, numberOfExecution);
* AE.Start();
* AE.SaveInformations(filename + ".txt");
* AE.ExportStatisticsToCsv(filename + "_stats.csv");
*
* // Test 3 - OX
* crossover = new OX();
* tsp = new TSPAlgorithm(numberOfEpoch, populationSize, testCities, parentSelection, mutation, crossover);
* AE = new AlgorithmExecutor(tsp, numberOfExecution);
* AE.Start();
* AE.SaveInformations(filename + ".txt");
* AE.ExportStatisticsToCsv(filename + "_stats.csv");
*
* // Test 4 - AEX
* crossover = new AEX();
* tsp = new TSPAlgorithm(numberOfEpoch, populationSize, testCities, parentSelection, mutation, crossover);
* AE = new AlgorithmExecutor(tsp, numberOfExecution);
* AE.Start();
* AE.SaveInformations(filename + ".txt");
* AE.ExportStatisticsToCsv(filename + "_stats.csv");
*
* // Test 5 - SCC
* crossover = new SCC();
* tsp = new TSPAlgorithm(numberOfEpoch, populationSize, testCities, parentSelection, mutation, crossover);
* AE = new AlgorithmExecutor(tsp, numberOfExecution);
* AE.Start();
* AE.SaveInformations(filename + ".txt");
* AE.ExportStatisticsToCsv(filename + "_stats.csv");
*
* //Test 6 - SCC
* crossover = new ClassicalCrossover();
* mutation = new ClassicMutationInOrdinalRepresentation(probabilityOfMutation);
* tsp = new TSPAlgorithm(numberOfEpoch, populationSize, testCities, parentSelection, mutation, crossover);
* AE = new AlgorithmExecutor(tsp, numberOfExecution);
* AE.Start();
* //AE.SaveInformations(filename + ".txt");
* //AE.ExportStatisticsToCsv(filename + "_stats.csv");
*/
Console.ReadKey();
}
private void btnStart_Click(object sender, EventArgs e)
{
Models.Instance i = new Models.Instance();
i.Days = 7;
i.Doctors = doctors;
int min = (int)numMin.Value;
int max = (int)numMax.Value;
var chromosome = new Models.Chromosome(21, i, r);
var population = new Population(min, max, chromosome);
var fitness = new Models.Fitness();
IMutation mutation = new TworsMutation();
ISelection selection = new RouletteWheelSelection();
ICrossover crossover = new OnePointCrossover(r.Next(20));
if (cbxMutation.SelectedItem.ToString() == "Insertion")
{
mutation = new InsertionMutation();
}
else if (cbxMutation.SelectedItem.ToString() == "Partial Shuffle")
{
mutation = new PartialShuffleMutation();
}
else if (cbxMutation.SelectedItem.ToString() == "Reverse Sequence")
{
mutation = new ReverseSequenceMutation();
}
if (cbxSelection.SelectedItem.ToString() == "Elitizam")
{
selection = new EliteSelection();
}
if (cbxCrossover.SelectedItem.ToString() == "Two-point")
{
int p1 = r.Next(19);
int p2 = r.Next(p1 + 1, 20);
crossover = new TwoPointCrossover(p1, p2);
}
else if (cbxCrossover.SelectedItem.ToString() == "Uniform")
{
crossover = new UniformCrossover();
}
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ITermination termination = new FitnessStagnationTermination(50);
if (cbxTermination.SelectedItem.ToString() == "Generation number")
{
termination = new GenerationNumberTermination(200);
}
ga.Termination = termination;
ga.MutationProbability = (float)numProbability.Value;
ga.Start();
Gene[] g = ga.BestChromosome.GetGenes();
dataView.Rows.Clear();
for (int j = 0; j < 7; j++)
{
string[] row = new string[] { ((List <int>)g[j * 3].Value)[0].ToString() + " " + ((List <int>)g[j * 3].Value)[1].ToString(),
((List <int>)g[j * 3 + 1].Value)[0].ToString() + " " + ((List <int>)g[j * 3 + 1].Value)[1].ToString(),
((List <int>)g[j * 3 + 2].Value)[0].ToString() + " " + ((List <int>)g[j * 3 + 2].Value)[1].ToString() };
dataView.Rows.Add(row);
dataView.Rows[j].HeaderCell.Value = (j + 1).ToString();
}
lblFitness.Text = ga.BestChromosome.Fitness.ToString() + " , generacija broj " + ga.GenerationsNumber.ToString();
}
