public static AbstractMutation ChosenMutationMethod(MutationMethod mutationMethod, double MutationProbability, int pointMutation)
{
AbstractMutation mutation = null;
switch (mutationMethod)
{
case MutationMethod.InversionMutation:
mutation = new InversionMutation(MutationProbability, pointMutation);
break;
case MutationMethod.ReplaceMutation:
mutation = new ReplaceMutaion(MutationProbability, pointMutation);
break;
case MutationMethod.SwapMutation:
mutation = new SwapMutation(MutationProbability, pointMutation);
break;
case MutationMethod.TranslocationMutation:
mutation = new TranslocationMutation(MutationProbability, pointMutation);
break;
case MutationMethod.NonMutation:
mutation = null;
break;
}
return(mutation);
}
public void ItErrorsIfTheIndexesAreOutOfRange()
{
var chromosome = GATestHelper.GetAlphabetCharacterChromosome();
var mutation = new InversionMutation();
mutation.Inverse(chromosome, -1, 1);
}
public void MutationTest1()
{
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);
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,
};
inv.Mutate(parentX);
}
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 ItCanInverseGenes()
{
var chromosome = GATestHelper.GetAlphabetCharacterChromosome();
var mutation = new InversionMutation();
mutation.Inverse(chromosome, 2, 6);
Assert.AreEqual("A,B,G,F,E,D,C,H,I,J", chromosome.ToString());
}
public void ItCanRandomlyInverseGenes()
{
var chromosome = GATestHelper.GetAlphabetCharacterChromosome();
var mutation = new InversionMutation();
for (int i = 0; i < 100; i++)
{
mutation.Mutate(chromosome, GATestHelper.GetTravelingSalesmanDefaultConfiguration());
}
}
public void Should_Perform_Mutation()
{
var algorithm = new InversionMutation(1);
var a = new Element(new double[] { 8D, 3D, 2D, 9D, 1D, 0D, 7D, 6D, 5D, 4D });
algorithm.Mutate(ref a, 1, 4);
var expectedData = new double[] { 8D, 1D, 9D, 2D, 3D, 0D, 7D, 6D, 5D, 4D };
Assert.Equal(expectedData, a.Data);
}
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);
}
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);
}
public void RouletteTests1()
{
int populationSize = 1000;
file = root + "\\bays29.xml";
XDocument tspFile = XDocument.Load(file);
AdjacencyMatrix testMatrix = new AdjacencyMatrix(tspFile);
PMXCrossover crossover = new PMXCrossover((float)(0.80));
RouletteSelection selector = new RouletteSelection(100);
InversionMutation inv = new InversionMutation((float)0.05);
GeneticSolver solver = new GeneticSolver(testMatrix, inv, crossover, selector, populationSize, 10);
List <Candidate> listCand = solver.randomPopulation();
listCand = selector.generateBreedingPool(listCand);
}
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 InversionMutation()
{
var settings = new GASettings();
var random = A.Fake <IRandom>();
var map = A.Dummy <Roteiro>();
var locals = GetLocals(5);
const int
indexTruck = 0,
indexLocalStart = 1,
length = 3;
A.CallTo(() => random.Next(A <int> ._, A <int> ._))
.ReturnsNextFromSequence(
indexTruck,
indexLocalStart,
length
);
var gen = new Genome(map, settings)
{
Trucks = new[] {
new Truck {
Locals = locals
}
}
};
var mutate = new InversionMutation(settings, random);
var newGen = mutate.Apply(gen);
var genLocals = newGen.Trucks[indexTruck].Locals;
var current = genLocals.Skip(indexLocalStart).Take(length).ToArray();
var expect = locals.Skip(indexLocalStart).Take(length).Reverse().ToArray();
current.Should().BeEquivalentTo(expect, o => o.WithStrictOrdering());
}
public void PMXTest1()
{
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);
//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,};
//PMXCrossover crossover = new PMXCrossover();
// crossover.Crossover(parentX, parentY);
}
internal GeneticSolver createNewSolver(bool isMultiThread)
{
MutationType mutation = null;
CrossoverType crossover = null;
SelectionType selection = null;
GeneticSolver solver = null;//add parameters TO DO
AdjacencyMatrix matrix = new AdjacencyMatrix(tspXmlFile);
switch (mutationIndex)
{
case 0:
{
if (!isMultiThread)
{
mutation = new InversionMutation(mutationChance);
}
else
{
mutation = new MultiThreadInversionMutation(mutationChance);
}
break;
}
case 1:
{
if (!isMultiThread)
{
mutation = new TranspositionMutation(mutationChance);
}
else
{
mutation = new MultiThreadTranspositionMutation(mutationChance);
}
break;
}
}
switch (crossoverIndex)
{
case 0:
{
if (!isMultiThread)
{
crossover = new PMXCrossover(crossoverChance);
}
else
{
crossover = new MultiThreadPMXCrossover(crossoverChance); //new PMXCrossover(crossoverChance); //
}
break;
}
case 1:
{
if (!isMultiThread)
{
crossover = new OXCrossover(crossoverChance);
}
else
{
crossover = new MultiThreadOXCrossover(crossoverChance);
}
break;
}
}
switch (selectorIndex)
{
case 0:
{
if (!isMultiThread)
{
selection = new TournamentSelection(selectorSize);
}
else
{
selection = new MultiThreadTournamentSelection(selectorSize);
}
break;
}
case 1:
{
if (!isMultiThread)
{
selection = new RouletteSelection(selectorSize);
}
else
{
selection = new MultiThreadRouletteSelection(selectorSize);
}
break;
}
}
if (mutation != null && selection != null && crossover != null)
{
if (isMultiThread)
{
MultiTaskOptions.parallelOptCrossover.MaxDegreeOfParallelism = int.Parse(View.tbxLvlCrossover.Text);
MultiTaskOptions.parallelOptMutation.MaxDegreeOfParallelism = int.Parse(View.tbxLvlMutation.Text);
MultiTaskOptions.parallelOptSelection.MaxDegreeOfParallelism = int.Parse(View.tbxLvlSelector.Text);
solver = new MultiTaskGeneticSolver(matrix, mutation, crossover, selection, populationSize, timeMS);
}
else
{
solver = new SingleTaskGeneticSolver(matrix, mutation, crossover, selection, populationSize, timeMS);
}
}
return(solver);
}
public void Setup()
{
inversionMutation = new InversionMutation();
}
