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 ItThrowsAnExceptionIfOutOfRange()
{
var chromosome = GATestHelper.GetAlphabetCharacterChromosome();
var mutation = new SwapMutation();
mutation.FlipGenes(chromosome, -1, 2);
}
public void SwapMutationTest()
{
IChromosome<bool> a = new BinaryChromosome().GenerateFromArray(new bool[] { false, true, false, true });
SwapMutation<bool> mutation = new SwapMutation<bool>(0, 3);
IChromosome<bool> res = mutation.Mutate(a);
IChromosome<bool> exp = new BinaryChromosome().GenerateFromArray(new bool[] { true, true, false, false });
Assert.AreEqual(exp, res);
}
public void ItCanFlipGenes()
{
var chromosome = GATestHelper.GetAlphabetCharacterChromosome();
var mutation = new SwapMutation();
mutation.FlipGenes(chromosome, 1, 2);
Assert.AreEqual("A,C,B,D,E,F,G,H,I,J", chromosome.ToString());
}
public void ItCanSwapGenesAndStaysWithinRange()
{
var chromosome = GATestHelper.GetAlphabetCharacterChromosome();
var mutation = new SwapMutation();
for (int i = 0; i < 100; i++)
{
mutation.Mutate(chromosome, GATestHelper.GetTravelingSalesmanDefaultConfiguration());
}
}
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 SwapMutation()
{
var settings = new GASettings();
var random = A.Fake <IRandom>();
var map = A.Dummy <Roteiro>();
var locals = GetLocals(4);
const int
indexTruck1 = 0,
indexTruck2 = 1,
indexLocal1 = 0,
indexLocal2 = 1;
A.CallTo(() => random.Next(A <int> ._, A <int> ._)).ReturnsNextFromSequence(indexTruck1, indexTruck2, indexLocal1, indexLocal2);
var gen = new Genome(map, settings)
{
Trucks = new[] {
new Truck {
Locals = new[] { locals[0], locals[1] }
},
new Truck {
Locals = new[] { locals[2], locals[3] }
}
}
};
var mutate = new SwapMutation(settings, random);
var newGen = mutate.Apply(gen);
newGen.Trucks[indexTruck1].Locals[indexLocal1]
.Should().BeEquivalentTo(
gen.Trucks[indexTruck2].Locals[indexLocal2],
o => o.WithStrictOrdering());
newGen.Trucks[indexTruck2].Locals[indexLocal2]
.Should().BeEquivalentTo(
gen.Trucks[indexTruck1].Locals[indexLocal1],
o => o.WithStrictOrdering());
}
public void SwapMutationTest()
{
Chromosome input = new Chromosome(5);
Chromosome output = new Chromosome(5);
input.Gen[0] = 1;
output.Gen[0] = 1;
SwapMutation mutate = new SwapMutation();
mutate.Init(new GeneticDataConfig()
{
Mutation = new GeneticDataConfig.MutationConfig()
{
MutationPercent = 1
},
GenCount = 5
});
mutate.Mutate(input);
CollectionAssert.AreNotEqual(input.Gen, output.Gen);
}
private static void TestGA(AEvaluation <int> problem, double probCrossover, double probMutation, int population, int iterations, double tournament)
{
List <String> results = new List <String>();
List <String> learningProgress = new List <String>();
int savedRun = new Random().Next(0, 10);
for (int i = 0; i < 10; i++)
{
//var evaluation = new TspEvaluation(@"C:\Users\jbelter\source\repos\machine-learning-cvrp\data\A-n32-k5.vrp");
var evaluation = problem;
var stopCondition = new IterationsStopCondition(iterations);
//var optimizer = new TspRandomSearch(evaluation, stopCondition);
var generator = new TspGenerator(new Random());
ASelection selection;
if (tournament > 1 && tournament <= population)
{
selection = new TournamentSelection(Convert.ToInt32(tournament));
}
else if (tournament > 0 && tournament <= 1)
{
selection = new TournamentSelection(tournament);
}
else
{
selection = new TournamentSelection(5);
}
var crossover = new OrderedCrossover(probCrossover);
var mutation = new SwapMutation(probMutation);
var optimizer = new GeneticAlgorithm <int>(evaluation, stopCondition, generator, selection, crossover, mutation, population);
optimizer.Run();
if (i == savedRun)
{
learningProgress = formatLearning(optimizer.worstValues, optimizer.averageValues, optimizer.bestSolutions);
}
//ReportOptimizationResult(optimizer.Result);
results.Add(String.Join(", ",
problem.iSize.ToString(),
optimizer.timeTaken.ToString(),
optimizer.bestSolutions.Last().ToString(),
optimizer.averageValues.Last().ToString(),
optimizer.worstValues.Last().ToString(),
probCrossover.ToString(),
probMutation.ToString(),
population.ToString(),
iterations.ToString(),
tournament.ToString()
));
}
SaveToFile(@"C:\Users\jbelter\source\repos\machine-learning-cvrp\resultsGA.txt", results);
SaveToFile((@"C:\Users\jbelter\source\repos\machine-learning-cvrp\progress\GA" + "-" +
problem.iSize.ToString() + "-" +
probCrossover.ToString() + "-" +
probMutation.ToString() + "-" +
population.ToString() + "-" +
iterations.ToString() + "-" +
tournament.ToString())
.Replace(".", "") + ".txt",
learningProgress);
}
public void ItErrorsIfSwapIsOutOfRange()
{
var mutation = new SwapMutation();
mutation.FlipGenes(GATestHelper.GetAlphabetCharacterChromosome(), -1, 3);
}
public void Setup()
{
swapMutation = new SwapMutation();
}
