public void SinglePointCrossoverApplyTest()
{
TestRandom random = new TestRandom();
IntegerVector parent1, parent2, expected, actual;
bool exceptionFired;
// The following test is not based on published examples
random.Reset();
random.IntNumbers = new int[] { 3 };
parent1 = new IntegerVector(new int[] { 2, 2, 3, 5, 1 });
parent2 = new IntegerVector(new int[] { 4, 1, 3, 2, 8 });
expected = new IntegerVector(new int[] { 2, 2, 3, 2, 8 });
actual = SinglePointCrossover.Apply(random, parent1, parent2);
Assert.IsTrue(Auxiliary.IntegerVectorIsEqualByPosition(actual, expected));
// The following test is not based on published examples
random.Reset();
random.IntNumbers = new int[] { 2 };
parent1 = new IntegerVector(new int[] { 2, 2, 3, 5, 1, 9 }); // this parent is longer
parent2 = new IntegerVector(new int[] { 4, 1, 3, 2, 8 });
exceptionFired = false;
try {
actual = SinglePointCrossover.Apply(random, parent1, parent2);
}
catch (System.ArgumentException) {
exceptionFired = true;
}
Assert.IsTrue(exceptionFired);
}
public void Should_cut_and_Produce_Child_properly()
{
for (int x = 0; x < 100; x++)
{
var parentA = new CrossoverTestModel(new[] { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 });
var parentB = new CrossoverTestModel(new[] { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 });
var options = new EvolutionOptions()
{
CollectionSize = parentA.Bits.Count
}; // 10 items
var child = new SinglePointCrossover <CrossoverTestModel>(options).Produce(parentA, parentB);
_output.WriteLine(child.ToString());
// asserts
var cutPoint = child.Bits.Select((b, i) => (bit: b, index: i)).First(e => e.bit == 1).index;
var bitsBeforeCutPoint = child.Bits.Where((b, i) => i < cutPoint).ToList();
var bitsAfterCutPoint = child.Bits.Where((b, i) => i >= cutPoint).ToList();
child.Bits.Should().HaveSameCount(parentA.Bits);
child.Bits.Should().HaveCount(bitsBeforeCutPoint.Count + bitsAfterCutPoint.Count);
bitsBeforeCutPoint.Should().AllBeEquivalentTo(0);
bitsAfterCutPoint.Should().AllBeEquivalentTo(1);
}
}
protected IBreeding <Synapse> NewBreeding(System.Random random)
{
ISelection <Synapse> selector;
if (roulteWheelSelection)
{
selector = new RouletteWheelSelection <Synapse>(random, 2);
}
else
{
selector = new EliteSelection <Synapse>(2);
}
var crossover = new SinglePointCrossover <Synapse>(random);
var mutator = new FixedNeuralNetMutation(
random,
geneMutChance,
mutRange);
return(new BreedingBase <Synapse>(
poplLne - (int)(poplLne * reinsertPart),
selector,
crossover,
mutator));
}
private BreedingBase <int> NewBreeding(
int poplLen,
double partToReinsert,
double geneMutChance,
int mutRange)
{
BreedingBase <int> result;
ISelection <int> selector;
ICrossover <int> crossover;
IMutation <int> mutator;
//selector = new EliteSelection<int>(2, partToBeElites: 0.5d);
selector = new RouletteWheelSelection <int>(RandomInst, 2);
crossover = new SinglePointCrossover <int>(RandomInst, 1);
mutator = new ES_Mutation(RandomInst, geneMutChance, mutRange);
result = new BreedingBase <int>(
poplLen - (int)(partToReinsert * poplLen),
selector,
crossover,
mutator
);
return(result);
}
public void ItThrowsAnErrorIfTheParentsAreNotLargeEnough()
{
var father = new OrderedChromosome(1);
var mother = new OrderedChromosome(1);
var singlePointCrossover = new SinglePointCrossover();
singlePointCrossover.Execute(father, mother, GATestHelper.GetTravelingSalesmanDefaultConfiguration());
}
public void ItThrowsAnErrorIfTheMotherIsNull()
{
var father = new OrderedChromosome(GATestHelper.GetRandomInteger(16, 32));
var mother = new OrderedChromosome(GATestHelper.GetRandomInteger(1, 8));
var singlePointCrossover = new SinglePointCrossover();
singlePointCrossover.Execute(father, null, GATestHelper.GetTravelingSalesmanDefaultConfiguration());
}
public void ItDoesNotThrowErrorsIfValidationPasses()
{
var size = GATestHelper.GetRandomInteger(16, 256);
var father = new OrderedChromosome(size);
var mother = new OrderedChromosome(size);
var singlePointCrossover = new SinglePointCrossover();
singlePointCrossover.Execute(father, mother, GATestHelper.GetTravelingSalesmanDefaultConfiguration());
}
public void ItCanEnsureTheMotherAndFatherPassAtLeastOneGene()
{
var father = GATestHelper.GetAlphabetCharacterChromosome();
var mother = GATestHelper.GetAlphabetCharacterChromosome();
mother.Genes.Shuffle(new Random());
var singlePoint = new SinglePointCrossover();
var child = singlePoint.Execute(father, mother, GATestHelper.GetTravelingSalesmanDefaultConfiguration());
Assert.AreNotEqual(child, father);
}
public void TestChromosome()
{
Chromosome a = new Chromosome(1000);
Chromosome b = new Chromosome(1000);
SinglePointCrossover spc = new SinglePointCrossover();
Chromosome c = spc.Cross(a, b);
for (int i = 0; i < c.Length; i++)
{
Assert.IsTrue(c.GetValue(i) == a.GetValue(i) || c.GetValue(i) == b.GetValue(i));
}
}
public static void RunCrossOver(List <CoupleParent> coupleParents, List <string> crossoverChildren, List <Person> population)
{
Random random = new Random();
int setCrossoverPoint = random.Next(0, 1);
if (setCrossoverPoint == 0)
{
SinglePointCrossover.DoCrossover(coupleParents, crossoverChildren, crossOverPercentage, population);
}
else
{
TwoPointCrossover.DoCrossover(coupleParents, crossoverChildren, crossOverPercentage, population);
}
}
public void ItCanPerformACrossover()
{
var father = GATestHelper.GetAlphabetCharacterChromosome();
var mother = GATestHelper.GetAlphabetCharacterChromosome();
mother.Genes.Shuffle(new Random());
var singlePoint = new SinglePointCrossover();
var child = singlePoint.Execute(father, mother, GATestHelper.GetTravelingSalesmanDefaultConfiguration());
Console.Out.WriteLine("Child: " + child.ToString());
Assert.AreNotEqual(father.ToString(), child.ToString());
Assert.AreNotEqual(mother.ToString(), child.ToString());
}
private IBreeding <Synapse> NewBreeding(System.Random random)
{
var selector = new EliteSelection <Synapse>(2);
//var selector = new RouletteWheelSelection<Synapse>(random, 2);
var crossover = new SinglePointCrossover <Synapse>(random);
var mutator = new FixedNeuralNetMutation(
random,
geneMutChance,
weightMutRange);
return(new BreedingBase <Synapse>(
poplLen - (int)(partToReinsert * poplLen),
selector,
crossover,
mutator));
}
static void Main(string[] args)
{
//var graph = new byte[,]
//{
// {0, 2, 3, 1, 3},
// {2, 0, 7, 6, 9},
// {3, 7, 0, 6, 4},
// {1, 6, 6, 0, 2},
// {3, 9, 4, 2, 0},
//};
Console.WriteLine("Enter number of cities:");
var n = int.Parse(Console.ReadLine());
var random = new Random();
var graph = new byte[n, n];
InitGraph(n, graph, random);
var selectionOperator = new RandomFromTheFittestSelection(Constants.CrossoverTopPercent, random);
var crossoverOperator = new SinglePointCrossover(random, (childPath => new Path(childPath, graph)));
var mutationOperator = new RandomSwapMutation(random, Constants.MutationChance, (childPath => new Path(childPath, graph)));
var pop = new Population(selectionOperator, crossoverOperator, mutationOperator, Constants.PopulationCount);
for (int i = 0; i < Constants.PopulationCount; i++)
{
pop.AddIndividual(new Path(GenerateRandomPath(n, random), graph));
}
var k = 1;
for (int i = 0; i < Constants.IterationsCount; i++)
{
pop.GenNextPopulation(Constants.ChildrenCountPercentage);
if (i == 2 * k)
{
k *= 2;
Console.WriteLine($"Shortest path for population {i} is: {pop.Individuals.First().Fitness}");
}
}
Console.WriteLine($"Shortest path for population {Constants.IterationsCount} is: {pop.Individuals.First().Fitness}");
}
private IBreeding <char> NewBreeding(
Random random,
int poplLen,
double partToReinsert,
double geneMutChance,
int mutRange)
{
var selector = new EliteSelection <char>(2);
//var selector = new RouletteWheelSelection<char>(random, 2);
var crossover = new SinglePointCrossover <char>(random);
var mutator = new Sh_Mutation(
random,
geneMutChance,
mutRange,
chars);
return(new BreedingBase <char>(
poplLen - (int)(partToReinsert * poplLen),
selector,
crossover,
mutator));
}
public void SinglePointCrossoverApplyTest()
{
TestRandom random = new TestRandom();
RealVector parent1, parent2, expected, actual;
bool exceptionFired;
// The following test is not based on published examples
random.Reset();
random.IntNumbers = new int[] { 3 };
parent1 = new RealVector(new double[] { 0.2, 0.2, 0.3, 0.5, 0.1 });
parent2 = new RealVector(new double[] { 0.4, 0.1, 0.3, 0.2, 0.8 });
expected = new RealVector(new double[] { 0.2, 0.2, 0.3, 0.2, 0.8 });
actual = SinglePointCrossover.Apply(random, parent1, parent2);
Assert.IsTrue(Auxiliary.RealVectorIsAlmostEqualByPosition(actual, expected));
// The following test is not based on published examples
random.Reset();
random.IntNumbers = new int[] { 2 };
parent1 = new RealVector(new double[] { 0.2, 0.2, 0.3, 0.5, 0.1, 0.9 }); // this parent is longer
parent2 = new RealVector(new double[] { 0.4, 0.1, 0.3, 0.2, 0.8 });
exceptionFired = false;
try {
actual = SinglePointCrossover.Apply(random, parent1, parent2);
}
catch (System.ArgumentException) {
exceptionFired = true;
}
Assert.IsTrue(exceptionFired);
// The following test is not based on published examples
random.Reset();
random.IntNumbers = new int[] { 5 }; // should not have an effect
parent1 = new RealVector(new double[] { 0.2, 0.4 });
parent2 = new RealVector(new double[] { 0.6, 0.1 });
expected = new RealVector(new double[] { 0.2, 0.1 });
actual = SinglePointCrossover.Apply(random, parent1, parent2);
Assert.IsTrue(Auxiliary.RealVectorIsAlmostEqualByPosition(actual, expected));
// The following test is not based on published examples
}
public static PRVEncoding Apply(IRandom random, PRVEncoding parent1, PRVEncoding parent2)
{
return(new PRVEncoding(SinglePointCrossover.Apply(random, parent1.PriorityRulesVector, parent2.PriorityRulesVector), parent1.NrOfRules));
}
