public void PartiallyMatchedCrossoverTest2()
{
Organism parent1 = new Organism();
Organism parent2 = new Organism();
parent1.Chromosomes.Add(new Chromosome(3, "001110"));
parent2.Chromosomes.Add(new Chromosome(3, "110001"));
IRandom rand = new Deterministic(1, 0);
PartiallyMatchedCrossover linker = new PartiallyMatchedCrossover(rand, 1);
var children = linker.CrossLink(parent1, parent2);
Assert.AreEqual("110001", children.Item1.ToString());
Assert.AreEqual("001110", children.Item2.ToString());
}
public void PmxOperatorShouldReturnCorrectChildren()
{
IGene[] parent1 = new IGene[]
{
new UintGene(1), new UintGene(2), new UintGene(3),
new UintGene(5), new UintGene(4), new UintGene(6),
new UintGene(7), new UintGene(8), new UintGene(9)
};
IGene[] parent2 = new IGene[]
{
new UintGene(4), new UintGene(5), new UintGene(2),
new UintGene(1), new UintGene(8), new UintGene(7),
new UintGene(6), new UintGene(9), new UintGene(3)
};
IGene[] expectedChild1 = new IGene[]
{
new UintGene(8), new UintGene(1), new UintGene(2),
new UintGene(5), new UintGene(4), new UintGene(6),
new UintGene(7), new UintGene(9), new UintGene(3)
};
IGene[] expectedChild2 = new IGene[]
{
new UintGene(5), new UintGene(2), new UintGene(3),
new UintGene(1), new UintGene(8), new UintGene(7),
new UintGene(6), new UintGene(4), new UintGene(9)
};
int cutPoint1 = 2, cutPoint2 = 6;
IGene[] child1, child2;
var pmx = new PartiallyMatchedCrossover();
pmx.Run(parent1, parent2, out child1, out child2, cutPoint1, cutPoint2);
bool areFirstChildrenEqual = true, areSecondChildrenEqual = true;
for (int i = 0; i < expectedChild1.Length; ++i)
{
areFirstChildrenEqual &= expectedChild1[i].Equals(child1[i]);
}
for (int i = 0; i < expectedChild2.Length; ++i)
{
areSecondChildrenEqual &= expectedChild2[i].Equals(child2[i]);
}
Assert.AreEqual(true, areFirstChildrenEqual);
Assert.AreEqual(true, areSecondChildrenEqual);
}
public void PartiallyMatchedCrossoverApplyTest()
{
TestRandom random = new TestRandom();
Permutation parent1, parent2, expected, actual;
// The following test is based on an example from Larranaga, 1999. Genetic Algorithms for the Traveling Salesman Problem.
random.Reset();
random.IntNumbers = new int[] { 3, 5 };
parent1 = new Permutation(PermutationTypes.RelativeUndirected, new int[] { 0, 1, 2, 3, 4, 5, 6, 7 });
Assert.IsTrue(parent1.Validate());
parent2 = new Permutation(PermutationTypes.RelativeUndirected, new int[] { 2, 6, 4, 0, 5, 7, 1, 3 });
Assert.IsTrue(parent2.Validate());
expected = new Permutation(PermutationTypes.RelativeUndirected, new int[] { 2, 6, 7, 3, 4, 5, 1, 0 });
Assert.IsTrue(expected.Validate());
actual = PartiallyMatchedCrossover.Apply(random, parent1, parent2);
Assert.IsTrue(actual.Validate());
Assert.IsTrue(Auxiliary.PermutationIsEqualByPosition(expected, actual));
// The following test is based on an example from Affenzeller, M. et al. 2009. Genetic Algorithms and Genetic Programming - Modern Concepts and Practical Applications. CRC Press. p. 134.
random.Reset();
random.IntNumbers = new int[] { 5, 7 };
parent1 = new Permutation(PermutationTypes.RelativeUndirected, new int[] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 });
Assert.IsTrue(parent1.Validate());
parent2 = new Permutation(PermutationTypes.RelativeUndirected, new int[] { 2, 5, 6, 0, 9, 1, 3, 8, 4, 7 });
Assert.IsTrue(parent2.Validate());
expected = new Permutation(PermutationTypes.RelativeUndirected, new int[] { 2, 1, 3, 0, 9, 5, 6, 7, 4, 8 });
Assert.IsTrue(expected.Validate());
actual = PartiallyMatchedCrossover.Apply(random, parent1, parent2);
Assert.IsTrue(actual.Validate());
Assert.IsTrue(Auxiliary.PermutationIsEqualByPosition(expected, actual));
// perform a test when the two permutations are of unequal length
random.Reset();
bool exceptionFired = false;
try {
PartiallyMatchedCrossover.Apply(random, new Permutation(PermutationTypes.RelativeUndirected, 8), new Permutation(PermutationTypes.RelativeUndirected, 6));
}
catch (System.ArgumentException) {
exceptionFired = true;
}
Assert.IsTrue(exceptionFired);
}
public override void Main()
{
DateTime start = DateTime.UtcNow;
QuadraticAssignmentProblem qap;
if (vars.Contains("qap"))
{
qap = vars.qap;
}
else
{
var provider = new DreznerQAPInstanceProvider();
var instance = provider.GetDataDescriptors().Single(x => x.Name == "dre56");
var data = provider.LoadData(instance);
qap = new QuadraticAssignmentProblem();
qap.Load(data);
vars.qap = qap;
}
const uint seed = 0;
const int popSize = 100;
const int generations = 1000;
const double mutationRate = 0.05;
var random = new MersenneTwister(seed);
var population = new Permutation[popSize];
var qualities = new double[popSize];
var nextGen = new Permutation[popSize];
var nextQual = new double[popSize];
var qualityChart = new DataTable("Quality Chart");
var qualityRow = new DataRow("Best Quality");
qualityChart.Rows.Add(qualityRow);
vars.qualityChart = qualityChart;
for (int i = 0; i < popSize; i++)
{
population[i] = new Permutation(PermutationTypes.Absolute, qap.Weights.Rows, random);
qualities[i] = QAPEvaluator.Apply(population[i], qap.Weights, qap.Distances);
}
var bestQuality = qualities.Min();
var bestQualityGeneration = 0;
for (int g = 0; g < generations; g++)
{
var parents = population.SampleProportional(random, 2 * popSize, qualities, windowing: true, inverseProportional: true).ToArray();
for (int i = 0; i < popSize; i++)
{
nextGen[i] = PartiallyMatchedCrossover.Apply(random, parents[i * 2], parents[i * 2 + 1]);
if (random.NextDouble() < mutationRate)
{
Swap2Manipulator.Apply(random, nextGen[i]);
}
nextQual[i] = QAPEvaluator.Apply(nextGen[i], qap.Weights, qap.Distances);
if (nextQual[i] < bestQuality)
{
bestQuality = nextQual[i];
bestQualityGeneration = g;
}
}
qualityRow.Values.Add(bestQuality);
Array.Copy(nextGen, population, popSize);
Array.Copy(nextQual, qualities, popSize);
}
vars.elapsed = new TimeSpanValue(DateTime.UtcNow - start);
vars.bestQuality = bestQuality;
vars.bestQualityFoundAt = bestQualityGeneration;
}
