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);
}
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 OXCrossover(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[] { 2D, 3D, 4D, 9D, 1D, 0D, 7D, 5D, 6D, 8D }, a.Data);
Assert.Equal(new double[] { 8D, 2D, 9D, 3D, 4D, 5D, 6D, 1D, 0D, 7D }, b.Data);
}
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);
}
