public IReinsertion Uniform()
{
var target = new UniformReinsertion();
target.SelectChromosomes(
new Population(5, 5, new TspChromosome(_numberOfCities)),
new List <IChromosome>
{
new TspChromosome(_numberOfCities)
},
_parents);
return(target);
}
public void SelectChromosomes_OffspringSizeEqualsZero_Exception()
{
var target = new UniformReinsertion();
var population = new Population(6, 8, MockRepository.GenerateStub <ChromosomeBase> (2));
var offspring = new List <IChromosome> ();
var parents = new List <IChromosome> ()
{
MockRepository.GenerateStub <ChromosomeBase> (5),
MockRepository.GenerateStub <ChromosomeBase> (6),
MockRepository.GenerateStub <ChromosomeBase> (7),
MockRepository.GenerateStub <ChromosomeBase> (8)
};
ExceptionAssert.IsThrowing(new ReinsertionException(target, "The minimum size of the offspring is 1."), () =>
{
target.SelectChromosomes(population, offspring, parents);
});
}
public void SelectChromosomes_OffspringSizeEqualsZero_Exception()
{
var target = new UniformReinsertion();
var population = new Population(6, 8, Substitute.For <ChromosomeBase>(2));
var offspring = new List <IChromosome>();
var parents = new List <IChromosome>()
{
Substitute.For <ChromosomeBase> (5),
Substitute.For <ChromosomeBase> (6),
Substitute.For <ChromosomeBase> (7),
Substitute.For <ChromosomeBase> (8)
};
Assert.Catch <ReinsertionException>(() =>
{
target.SelectChromosomes(population, offspring, parents);
}, "The minimum size of the offspring is 1.");
}
public void SelectChromosomes_offspringSizeLowerThanMinSize_Selectoffspring()
{
var target = new UniformReinsertion();
var population = new Population(6, 8, MockRepository.GenerateStub <ChromosomeBase> (2));
var offspring = new List <IChromosome> ()
{
MockRepository.GenerateStub <ChromosomeBase> (2),
MockRepository.GenerateStub <ChromosomeBase> (2),
MockRepository.GenerateStub <ChromosomeBase> (3),
MockRepository.GenerateStub <ChromosomeBase> (4)
};
var parents = new List <IChromosome> ()
{
MockRepository.GenerateStub <ChromosomeBase> (5),
MockRepository.GenerateStub <ChromosomeBase> (6),
MockRepository.GenerateStub <ChromosomeBase> (7),
MockRepository.GenerateStub <ChromosomeBase> (8)
};
var rnd = MockRepository.GenerateMock <IRandomization> ();
rnd.Expect(r => r.GetInt(0, 4)).Return(1);
rnd.Expect(r => r.GetInt(0, 5)).Return(3);
RandomizationProvider.Current = rnd;
var selected = target.SelectChromosomes(population, offspring, parents);
Assert.AreEqual(6, selected.Count);
Assert.AreEqual(2, selected [0].Length);
Assert.AreEqual(2, selected [1].Length);
Assert.AreEqual(3, selected [2].Length);
Assert.AreEqual(4, selected [3].Length);
Assert.AreEqual(2, selected [4].Length);
Assert.AreEqual(4, selected [5].Length);
}
public void SelectChromosomes_offspringSizeLowerThanMinSize_Selectoffspring()
{
var target = new UniformReinsertion();
var population = new Population(6, 8, Substitute.For <ChromosomeBase>(2));
var offspring = new List <IChromosome>()
{
Substitute.For <ChromosomeBase> (2),
Substitute.For <ChromosomeBase> (2),
Substitute.For <ChromosomeBase> (3),
Substitute.For <ChromosomeBase> (4)
};
var parents = new List <IChromosome>()
{
Substitute.For <ChromosomeBase> (5),
Substitute.For <ChromosomeBase> (6),
Substitute.For <ChromosomeBase> (7),
Substitute.For <ChromosomeBase> (8)
};
var rnd = Substitute.For <IRandomization>();
rnd.GetInt(0, 4).Returns(1);
rnd.GetInt(0, 5).Returns(3);
RandomizationProvider.Current = rnd;
var selected = target.SelectChromosomes(population, offspring, parents);
Assert.AreEqual(6, selected.Count);
Assert.AreEqual(2, selected[0].Length);
Assert.AreEqual(2, selected[1].Length);
Assert.AreEqual(3, selected[2].Length);
Assert.AreEqual(4, selected[3].Length);
Assert.AreEqual(2, selected[4].Length);
Assert.AreEqual(4, selected[5].Length);
}
private static IAlgoritmo CriaAlgoritmoGenetico(Dictionary <string, string[]> dict, List <string> flat, Problema problema)
{
int populacaoMin, populacaoMax;
IPopulation population;
ISelection selection;
ICrossover crossover;
IMutation mutation;
ITermination termination;
IReinsertion reinsertion;
float crossoverProbability, mutationProbability;
var p = dict.ValueOrDefault("p", "50,100").Split(new[] { ',' });
if (p.Length != 2 || !int.TryParse(p[0], out populacaoMin) || !int.TryParse(p[1], out populacaoMax))
{
throw new ArgumentException("Faixa de população inválida.");
}
population = new Population(populacaoMin, populacaoMax, new CromossomoViajante(problema.Mapa.Locais.Count));
switch (dict.ValueOrDefault("s", "t"))
{
case "e":
selection = new EliteSelection();
break;
case "r":
selection = new RouletteWheelSelection();
break;
case "s":
selection = new StochasticUniversalSamplingSelection();
break;
case "t":
selection = new TournamentSelection();
break;
default:
throw new ArgumentException("Seleção inválida.");
}
switch (dict.ValueOrDefault("c", "o"))
{
case "s":
crossover = new CutAndSpliceCrossover();
break;
case "c":
crossover = new CycleCrossover();
break;
case "o":
crossover = new OrderedCrossover();
break;
case "ob":
crossover = new OrderBasedCrossover();
break;
case "op":
crossover = new OnePointCrossover();
break;
case "pm":
crossover = new PartiallyMappedCrossover();
break;
case "p":
crossover = new PositionBasedCrossover();
break;
case "tpa":
crossover = new ThreeParentCrossover();
break;
case "tp":
crossover = new TwoPointCrossover();
break;
case "u":
crossover = new UniformCrossover();
break;
default:
throw new ArgumentException("Crossover inválido.");
}
switch (dict.ValueOrDefault("m", "r"))
{
case "d":
mutation = new DisplacementMutation();
break;
case "f":
mutation = new FlipBitMutation();
break;
case "i":
mutation = new InsertionMutation();
break;
case "s":
mutation = new PartialShuffleMutation();
break;
case "r":
mutation = new ReverseSequenceMutation();
break;
case "t":
mutation = new TworsMutation();
break;
case "u":
mutation = new UniformMutation();
break;
default:
throw new ArgumentException("Mutação inválida.");
}
switch (dict.ValueOrDefault("t", "s"))
{
case "s":
termination = new FitnessStagnationTermination();
break;
case "t":
termination = new FitnessThresholdTermination();
break;
case "g":
termination = new GenerationNumberTermination();
break;
default:
throw new ArgumentException("Terminação inválida.");
}
switch (dict.ValueOrDefault("e", "e"))
{
case "e":
reinsertion = new ElitistReinsertion();
break;
case "p":
reinsertion = new PureReinsertion();
break;
case "u":
reinsertion = new UniformReinsertion();
break;
default:
throw new ArgumentException("Reinserção inválida.");
}
if (!float.TryParse(dict.ValueOrDefault("cp", "0,75"), out crossoverProbability))
{
throw new ArgumentException("Probabilidade de crossover inválida.");
}
if (!float.TryParse(dict.ValueOrDefault("mp", "0,25"), out mutationProbability))
{
throw new ArgumentException("Probabilidade de mutação inválida.");
}
return(new AlgoritmoGenetico(problema, population, selection, crossover, crossoverProbability, mutation, mutationProbability, termination, reinsertion));
}
