/// <summary>
/// Initializes a new instance of the <see cref="GeneticSharp.Domain.GeneticAlgorithm"/> class.
/// </summary>
/// <param name="population">The chromosomes population.</param>
/// <param name="fitness">The fitness evaluation function.</param>
/// <param name="selection">The selection operator.</param>
/// <param name="crossover">The crossover operator.</param>
/// <param name="mutation">The mutation operator.</param>
public GeneticAlgorithm(
IPopulation population,
IFitness fitness,
ISelection selection,
ICrossover crossover,
IMutation mutation)
{
ExceptionHelper.ThrowIfNull("population", population);
ExceptionHelper.ThrowIfNull("fitness", fitness);
ExceptionHelper.ThrowIfNull("selection", selection);
ExceptionHelper.ThrowIfNull("crossover", crossover);
ExceptionHelper.ThrowIfNull("mutation", mutation);
Population = population;
Fitness = fitness;
Selection = selection;
Crossover = crossover;
Mutation = mutation;
Reinsertion = new ElitistReinsertion();
Termination = new GenerationNumberTermination(1);
CrossoverProbability = DefaultCrossoverProbability;
MutationProbability = DefaultMutationProbability;
TimeEvolving = TimeSpan.Zero;
State = GeneticAlgorithmState.NotStarted;
TaskExecutor = new LinearTaskExecutor();
}
public IReinsertion Elitist()
{
var target = new ElitistReinsertion();
target.SelectChromosomes(new Population(10, 10, new TspChromosome(_numberOfCities)), new List <IChromosome>(), _parents);
return(target);
}
public void SelectChromosomes_offspringSizeLowerThanMinSize_Selectoffspring()
{
var target = new ElitistReinsertion();
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)
};
parents [0].Fitness = 0.2;
parents [1].Fitness = 0.3;
parents [2].Fitness = 0.5;
parents [3].Fitness = 0.7;
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(8, selected [4].Length);
Assert.AreEqual(7, 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));
}
public static DynamoGeneticAlgorithm CreateGeneticAlgorithm(Population population,
[DefaultArgument("Evo.DynamoGeneticAlgorithm.Default()")] object selection,
[DefaultArgument("Evo.DynamoGeneticAlgorithm.Default()")] object crossover,
[DefaultArgument("Evo.DynamoGeneticAlgorithm.Default()")] object mutation,
[DefaultArgument("Evo.DynamoGeneticAlgorithm.Default()")] object termination,
[DefaultArgument("Evo.DynamoGeneticAlgorithm.Default()")] int?selectionSize,
double crossoverProbability = DynamoGeneticAlgorithm.DefaultCrossoverProbability,
double mutationProbability = DynamoGeneticAlgorithm.DefaultMutationProbability)
{
IChromosome exemplaryChromome = population.CurrentGeneration.Chromosomes[0];
ChromosomeType chromosomeType = 0;
if (exemplaryChromome is BinaryChromosome)
{
chromosomeType = ((BinaryChromosome)exemplaryChromome).ChromosomeType;
}
else if (exemplaryChromome is CombinatorialChromosome)
{
chromosomeType = ((CombinatorialChromosome)exemplaryChromome).ChromosomeType;
}
else if (exemplaryChromome is DoubleChromosome)
{
chromosomeType = ((DoubleChromosome)exemplaryChromome).ChromosomeType;
}
ISelection selectionMethod = null;
if (selection == null)
{
selectionMethod = new EliteSelection();
}
else
{
if (selection is EliteSelection)
{
selectionMethod = selection as EliteSelection;
}
else if (selection is RouletteWheelSelection)
{
selectionMethod = selection as RouletteWheelSelection;
}
else if (selection is StochasticUniversalSamplingSelection)
{
selectionMethod = selection as StochasticUniversalSamplingSelection;
}
else if (selection is TournamentSelection)
{
selectionMethod = selection as TournamentSelection;
}
else
{
throw new CrossoverException("Invalid selection input. A valid object returned by a node of the Selections category should be used.");
}
}
ICrossover crossoverMethod = null;
if (crossover == null)
{
crossoverMethod = new UniformCrossover(0.5f);
}
else
{
if (crossover is AlternatingPositionCrossover)
{
if (chromosomeType != ChromosomeType.CombinatorialChromosome)
{
throw new CrossoverException("The Alternating-position Crossover (AP) can be only used with combinatorial chromosomes. The specified individuals are not combinatorial chromosomes.");
}
crossoverMethod = crossover as AlternatingPositionCrossover;
}
else if (crossover is CutAndSpliceCrossover)
{
if (chromosomeType == ChromosomeType.DoubleChromosome)
{
throw new CrossoverException("The Cut and Splice Crossover (AP) can be only used with binary and combinatorial chromosomes. The specified individuals are double chromosomes.");
}
crossoverMethod = crossover as CutAndSpliceCrossover;
}
else if (crossover is CycleCrossover)
{
if (chromosomeType != ChromosomeType.CombinatorialChromosome)
{
throw new CrossoverException("The Cycle Crossover (CX) can be only used with combinatorial chromosomes. The specified individuals are not combinatorial chromosomes.");
}
crossoverMethod = crossover as CycleCrossover;
}
else if (crossover is OnePointCrossover)
{
if (chromosomeType == ChromosomeType.DoubleChromosome)
{
throw new CrossoverException("The One-point Crossover can be only used with binary and combinatorial chromosomes. The specified individuals are double chromosomes.");
}
crossoverMethod = crossover as OnePointCrossover;
}
else if (crossover is OrderBasedCrossover)
{
if (chromosomeType != ChromosomeType.CombinatorialChromosome)
{
throw new CrossoverException("The Order Based Crossover (OX2) can be only used with combinatorial chromosomes. The specified individuals are not combinatorial chromosomes.");
}
crossoverMethod = crossover as OrderBasedCrossover;
}
else if (crossover is OrderedCrossover)
{
if (chromosomeType != ChromosomeType.CombinatorialChromosome)
{
throw new CrossoverException("The Ordered Crossover (OX1) can be only used with combinatorial chromosomes. The specified individuals are not combinatorial chromosomes.");
}
crossoverMethod = crossover as OrderedCrossover;
}
else if (crossover is PartiallyMappedCrossover)
{
if (chromosomeType != ChromosomeType.CombinatorialChromosome)
{
throw new CrossoverException("The Partially-mapped Crossover (PMX) can be only used with combinatorial chromosomes. The specified individuals are not combinatorial chromosomes.");
}
crossoverMethod = crossover as PartiallyMappedCrossover;
}
else if (crossover is PositionBasedCrossover)
{
if (chromosomeType != ChromosomeType.CombinatorialChromosome)
{
throw new CrossoverException("The Position Based Crossover (POS) can be only used with combinatorial chromosomes. The specified individuals are not combinatorial chromosomes.");
}
crossoverMethod = crossover as PositionBasedCrossover;
}
else if (crossover is SimulatedBinaryCrossover)
{
if (chromosomeType != ChromosomeType.DoubleChromosome)
{
throw new CrossoverException("The Simulated Binary Crossover (SBX) can be only used with double chromosomes. The specified individuals are not double chromosomes.");
}
crossoverMethod = crossover as SimulatedBinaryCrossover;
}
else if (crossover is ThreeParentCrossover)
{
if (chromosomeType == ChromosomeType.DoubleChromosome)
{
throw new CrossoverException("The Three-parent Crossover can be only used with binary and combinatorial chromosomes. The specified individuals are double chromosomes.");
}
crossoverMethod = crossover as ThreeParentCrossover;
}
else if (crossover is TwoPointCrossover)
{
if (chromosomeType == ChromosomeType.DoubleChromosome)
{
throw new CrossoverException("The Two-point Crossover can be only used with binary and combinatorial chromosomes. The specified individuals are double chromosomes.");
}
crossoverMethod = crossover as TwoPointCrossover;
}
else if (crossover is UniformCrossover)
{
if (chromosomeType == ChromosomeType.DoubleChromosome)
{
throw new CrossoverException("The Uniform Crossover can be only used with binary and combinatorial chromosomes. The specified individuals are double chromosomes.");
}
crossoverMethod = crossover as UniformCrossover;
}
else if (crossover is VotingRecombinationCrossover)
{
if (chromosomeType == ChromosomeType.DoubleChromosome)
{
throw new CrossoverException("The Voting Recombination Crossover (VR) can be only used with binary and combinatorial chromosomes. The specified individuals are double chromosomes.");
}
crossoverMethod = crossover as VotingRecombinationCrossover;
}
else
{
throw new CrossoverException("Invalid crossover input. A valid object returned by a node of the Crossovers category should be used.");
}
}
IMutation mutationMethod = null;
if (mutation == null)
{
mutationMethod = new FlipBitMutation();
}
else
{
if (mutation is DisplacementMutation)
{
if (chromosomeType == ChromosomeType.DoubleChromosome)
{
throw new CrossoverException("The Displacement Mutation can be only used on binary and combinatorial chromosomes. The specified individuals are double chromosomes.");
}
mutationMethod = mutation as DisplacementMutation;
}
else if (mutation is FlipBitMutation)
{
if (chromosomeType != ChromosomeType.BinaryChromosome)
{
throw new CrossoverException("The Flip Bit Mutation can be only used on binary chromosomes. The specified individuals are not binary chromosomes.");
}
mutationMethod = mutation as FlipBitMutation;
}
else if (mutation is GaussianMutation)
{
if (chromosomeType != ChromosomeType.DoubleChromosome)
{
throw new CrossoverException("The Gaussian Mutation can be only used on double chromosomes. The specified individuals are not double chromosomes.");
}
mutationMethod = mutation as GaussianMutation;
}
else if (mutation is InsertionMutation)
{
if (chromosomeType == ChromosomeType.DoubleChromosome)
{
throw new CrossoverException("The Displacement Mutation can be only used on binary and combinatorial chromosomes. The specified individuals are double chromosomes.");
}
mutationMethod = mutation as InsertionMutation;
}
else if (mutation is PartialShuffleMutation)
{
if (chromosomeType == ChromosomeType.DoubleChromosome)
{
throw new CrossoverException("The Displacement Mutation (PSM) can be only used on binary and combinatorial chromosomes. The specified individuals are double chromosomes.");
}
mutationMethod = mutation as PartialShuffleMutation;
}
else if (mutation is PolynomialMutation)
{
if (chromosomeType != ChromosomeType.DoubleChromosome)
{
throw new CrossoverException("The Polynomial Mutation can be only used on double chromosomes. The specified individuals are not double chromosomes.");
}
mutationMethod = mutation as PolynomialMutation;
}
else if (mutation is ReverseSequenceMutation)
{
if (chromosomeType == ChromosomeType.DoubleChromosome)
{
throw new CrossoverException("The Displacement Mutation (RSM) can be only used on binary and combinatorial chromosomes. The specified individuals are double chromosomes.");
}
mutationMethod = mutation as ReverseSequenceMutation;
}
else if (mutation is TworsMutation)
{
if (chromosomeType == ChromosomeType.DoubleChromosome)
{
throw new CrossoverException("The Displacement Mutation can be only used on binary and combinatorial chromosomes. The specified individuals are double chromosomes.");
}
mutationMethod = mutation as TworsMutation;
}
else if (mutation is UniformMutation)
{
if (chromosomeType != ChromosomeType.BinaryChromosome)
{
throw new CrossoverException("The Uniform Mutation can be only used on binary chromosomes. The specified individuals are not binary chromosomes.");
}
mutationMethod = mutation as UniformMutation;
}
else
{
throw new CrossoverException("Invalid mutation input. A valid object returned by a node of Mutations category should be used.");
}
}
ElitistReinsertion reinsertionMethod = new ElitistReinsertion();
ITermination terminationMethod = null;
if (termination == null)
{
terminationMethod = new FitnessStagnationTermination(100);
}
else
{
if (termination is FitnessStagnationTermination)
{
terminationMethod = termination as FitnessStagnationTermination;
}
else if (termination is FitnessThresholdTermination)
{
terminationMethod = termination as FitnessThresholdTermination;
}
else if (termination is GenerationNumberTermination)
{
terminationMethod = termination as GenerationNumberTermination;
}
else if (termination is TimeEvolvingTermination)
{
terminationMethod = termination as TimeEvolvingTermination;
}
else if (termination is AndTermination)
{
terminationMethod = termination as AndTermination;
}
else if (termination is OrTermination)
{
terminationMethod = termination as OrTermination;
}
else
{
throw new CrossoverException("Invalid termination input. An object returned by nodes of Terminations library should be used.");
}
}
if (selectionSize == null)
{
selectionSize = (int)Math.Round(0.5 * population.MaxSize);
}
else if (!(selectionSize is int))
{
throw new CrossoverException("Defined selection size is not an integer.");
}
else if (selectionSize > population.MaxSize)
{
throw new CrossoverException("Selection size cannot be greater than population size.");
}
var algorithm = new DynamoGeneticAlgorithm(population, selectionMethod, crossoverMethod, mutationMethod, reinsertionMethod, terminationMethod)
{
SelectionSize = (int)selectionSize,
CrossoverProbability = (float)crossoverProbability,
MutationProbability = (float)mutationProbability,
Timer = Stopwatch.StartNew()
};
algorithm.Timer.Start();
return(algorithm);
}
public void Solve(Maze maze)
{
const int NumberOfGenes = 5000;
const float MutationRate = 0.02f;
const float CrossOverRate = 0.6f;
var selection = new EliteSelection();
var crossover = new UniformCrossover();
var mutation = new UniformMutation(true);
var chromosome = new MazeSolverChromosome(NumberOfGenes);
var reinsertion = new ElitistReinsertion();
var population = new Population(50, 100, chromosome);
var fitness = new MazeWalkerFitness(maze);
IChromosome best = chromosome;
best.Fitness = fitness.Evaluate(best);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.MutationProbability = MutationRate;
ga.Reinsertion = reinsertion;
ga.Termination = new OrTermination(
new FitnessStagnationTermination(this.stagnationThreshold),
new TimeEvolvingTermination(TimeSpan.FromSeconds(this.maxRunTimeInSeconds)));
ga.CrossoverProbability = CrossOverRate;
ga.TaskExecutor = new SmartThreadPoolTaskExecutor()
{
MinThreads = Environment.ProcessorCount,
MaxThreads = Environment.ProcessorCount
};
ga.GenerationRan += (sender, eventargs) =>
{
if (this.generateUpdateImages)
{
if (ga.GenerationsNumber == 1 || ga.GenerationsNumber % this.updateImageFrequency == 0)
{
var winnerSteps = FillMazeStepsWalked(maze, ga.BestChromosome);
ImageDraw(winnerSteps, $"gen{ga.GenerationsNumber}.png");
}
}
if (ga.GenerationsNumber % 10 == 0)
{
Console.WriteLine(
$"{ga.GenerationsNumber} generations completed. Best fitness: {ga.BestChromosome.Fitness}. Best so far: {best.Fitness}. Time evolving: {ga.TimeEvolving.TotalMinutes} min.");
}
if (ga.BestChromosome.Fitness > best.Fitness)
{
best = ga.BestChromosome.Clone();
// ga.Population.CurrentGeneration.Chromosomes.Add(best);
}
};
ga.TerminationReached += (sender, eventargs) =>
{
Console.WriteLine($"Termination Reached");
var winnerSteps = FillMazeStepsWalked(maze, best);
ImageDraw(winnerSteps, "best.png");
var serializer = new XmlSerializer(typeof(string[]));
using (var sw = new StreamWriter("bestchromosome.xml"))
{
serializer.Serialize(sw, best.GetGenes().Select(g => g.Value as string).ToArray());
}
if (this.animate)
{
File.Delete("output.gif");
this.gif.Save("output.gif");
}
};
if (this.generateUpdateImages)
{
this.ImageDraw(maze, "gen0.png");
}
ga.Start();
}
