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);
}
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 Should_Perform_Crossover()
{
var algorithm = new PMXCrossover(1);
var a = new Element(new double[] { 0D, 1D, 2D, 3D, 4D, 5D, 6D, 7D, 8D, 9D });
var b = new Element(new double[] { 8D, 3D, 2D, 9D, 1D, 0D, 7D, 6D, 5D, 4D });
algorithm.Crossover(ref a, ref b, 2, 6);
Assert.Equal(new double[] { 5D, 4D, 2D, 9D, 1D, 0D, 7D, 6D, 8D, 3D }, a.Data);
Assert.Equal(new double[] { 8D, 9D, 2D, 3D, 4D, 5D, 6D, 7D, 0D, 1D }, b.Data);
}
public void PMXCrossover_Cross_ReturnChildren(int position, int length, int[] expected)
{
var random = new PredeterminedRandom(new double[] { position, length });
var parameterSet = new ParameterSet();
var crossover = new PMXCrossover(random, parameterSet, null);
var parents = PermutationTestData.CreateTestChromosomes();
var children = crossover.Cross(parents[0], parents[1]);
children.Count.Should().Be(2);
((PermutationChromosome)children[0]).Genes.Should().BeEquivalentTo(expected);
}
public void PMXCrossover_CreateGeneMap_GetUniqueValue_ReturnUnique(int position, int expected)
{
var random = new PredeterminedRandom(new double[0]);
var parameterSet = new ParameterSet();
var crossover = new PMXCrossover(random, parameterSet, null);
var chromosomes = PermutationTestData.CreateTestChromosomes();
var range = new GeneRange(3, 4);
var geneMap = crossover.GenerateGeneMap(((PermutationChromosome)chromosomes[0]).Genes, range);
var result = crossover.GetUniqueGeneValue(((PermutationChromosome)chromosomes[1]).Genes, geneMap, position);
result.Should().Be(expected);
}
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 PMXCrossover_CopyWithDuplicationElimination_CopyData(int begin, int end, int[] expected)
{
var random = new PredeterminedRandom(new double[0]);
var parameterSet = new ParameterSet();
var crossover = new PMXCrossover(random, parameterSet, null);
var chromosomes = PermutationTestData.CreateTestChromosomes();
var range = new GeneRange(3, 3);
var geneMap = crossover.GenerateGeneMap(((PermutationChromosome)chromosomes[0]).Genes, range);
var childGenes = new int[((PermutationChromosome)chromosomes[0]).Genes.Length];
var length = end - begin;
crossover.CopyWithDuplicationElimination(((PermutationChromosome)chromosomes[1]).Genes, childGenes, geneMap, begin, end);
var result = new int[length];
Array.Copy(childGenes, begin, result, 0, length);
result.Should().BeEquivalentTo(expected);
}
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()
{
pmxCrossover = new PMXCrossover();
}
