public void Evolve_ManyGenerations_Fast()
{
int numberOfCities = 40;
var selection = new EliteSelection();
var crossover = new OrderedCrossover();
var mutation = new TworsMutation();
var chromosome = new TspChromosome(numberOfCities);
var fitness = new TspFitness(numberOfCities, 0, 1000, 0, 1000);
var population = new Population(40, 40, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Start();
var firstDistance = ((TspChromosome)ga.Population.BestChromosome).Distance;
ga.Termination = new GenerationNumberTermination(1001);
TimeAssert.LessThan(100000, () =>
{
ga.Start();
});
var lastDistance = ((TspChromosome)ga.Population.BestChromosome).Distance;
Assert.Less(lastDistance, firstDistance);
}
private void Optimize_Load(object sender, EventArgs e)
{
var selection = new EliteSelection();
var crossover = new OrderedCrossover();
var mutation = new ReverseSequenceMutation();
var fitness = new TrajectoryFitness();
var chromosome = new TrajectoryChromosome(TrajectoryChromosome.ChromosomeLength(3), 3);
var population = new Population(35, 40, chromosome);
//72 (6)
//67 (2)
//49 (3)
//102 (1)
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
var executor = new SmartThreadPoolTaskExecutor();
executor.MinThreads = 1;
executor.MaxThreads = 1;
ga.TaskExecutor = executor;
ga.Termination = new GenerationNumberTermination(10);
Console.WriteLine("GA running...");
var t = DateTime.Now;
ga.Start();
Console.WriteLine("Time:" + DateTime.Now.Subtract(t).TotalSeconds);
Console.WriteLine("Best solution found has {0} fitness.", int.MaxValue - ga.BestChromosome.Fitness);
Console.WriteLine(ga.BestChromosome.GetGene(0).Value + " " + ga.BestChromosome.GetGene(1).Value + " " + ga.BestChromosome.GetGene(2).Value);
}
public static void ga()
{
AlgorithmList.loadformnistfsgm();
var selection = new EliteSelection();
var crossover = new OrderedCrossover();
var mutation = new ReverseSequenceMutation();
var fitness = new MyProblemFitness();
var chromosome = new MyChromosome();
var population = new Population(4, 6, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new GenerationNumberTermination(4);
Console.WriteLine("GA running...");
ga.Start();
Console.WriteLine("Best solution found has {0} fitness.", ga.BestChromosome.Fitness);
var a = ga.BestChromosome.GetGenes();
string s = "";
foreach (var b in a)
{
s = s + b.Value.ToString();
}
Console.WriteLine("Best solution found has {0} ", s);
}
private void Start()
{
var fitness = new TspFitness(m_numberOfCities);
var chromosome = new TspChromosome(m_numberOfCities);
// This operators are classic genetic algorithm operators that lead to a good solution on TSP,
// but you can try others combinations and see what result you get.
var crossover = new OrderedCrossover();
var mutation = new ReverseSequenceMutation();
var selection = new RouletteWheelSelection();
var population = new Population(50, 100, chromosome);
m_ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
m_ga.Termination = new TimeEvolvingTermination(System.TimeSpan.FromHours(1));
// The fitness evaluation of whole population will be running on parallel.
m_ga.TaskExecutor = new ParallelTaskExecutor
{
MinThreads = 100,
MaxThreads = 200
};
// Everty time a generation ends, we log the best solution.
m_ga.GenerationRan += delegate
{
var distance = ((TspChromosome)m_ga.BestChromosome).Distance;
Debug.Log($"Generation: {m_ga.GenerationsNumber} - Distance: ${distance}");
};
DrawCities();
// Starts the genetic algorithm in a separate thread.
m_gaThread = new Thread(() => m_ga.Start());
m_gaThread.Start();
}
public void Evolve_ManyGenerations_Fast()
{
int movesAhead = 10;
int boardSize = 10;
var selection = new EliteSelection();
var crossover = new OrderedCrossover();
var mutation = new TworsMutation();
var chromosome = new CheckersChromosome(movesAhead, boardSize);
var fitness = new CheckersFitness(new CheckersBoard(boardSize));
var population = new Population(40, 40, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.GenerationRan += delegate
{
if (ga.Population.GenerationsNumber % 100 == 0)
{
fitness.Update(ga.Population.BestChromosome as CheckersChromosome);
}
};
ga.Start();
var firstFitness = ((CheckersChromosome)ga.Population.BestChromosome).Fitness;
ga.Termination = new GenerationNumberTermination(2001);
ga.Start();
var lastFitness = ((CheckersChromosome)ga.Population.BestChromosome).Fitness;
Assert.LessOrEqual(firstFitness, lastFitness);
}
static GeneticAlgorithm CreateGA(IPopulation population)
{
var selection = new EliteSelection();
var crossover = new OrderedCrossover();
var mutation = new ReverseSequenceMutation();
var fitness = new TspFitness(100, 0, 1000, 0, 1000);
return(new GeneticAlgorithm(population, fitness, selection, crossover, mutation));
}
public void Initialize(int numberOfCities, int areaWidth, int areaHeight)
{
Stop();
Fitness = new TspFitness(numberOfCities, areaWidth, areaHeight);
var chromosome = new TspChromosome(numberOfCities);
// This operators are classic genetic algorithm operators that lead to a good solution on TSP,
// but you can try others combinations and see what result you get.
var crossover = new OrderedCrossover();
var mutation = new ReverseSequenceMutation();
var selection = new RouletteWheelSelection();
var population = new Population(50, 100, chromosome);
_ga = new GeneticAlgorithm(population, Fitness, selection, crossover, mutation);
}
public void LinesOfCode()
{
const string source = @"
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.Diagnostics;
namespace ConsoleApplication1
{
class Calculations
{
double CalculateTotal(int quantity, int itemPrice)
{
double basePrice = quantity * itemPrice;
if (basePrice > 1000)
{
return basePrice * 0.95;
}
else
{
return basePrice * 0.98;
}
}
}
}";
var selection = new EliteSelection();
var crossover = new OrderedCrossover();
var mutation = new ReverseSequenceMutation();
var fitness = new MetricsFitness <LinesOfCodeMetrics>();
var chromosome = new RefactoringChromosome(3, source);
var population = new Population(20, 30, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new GenerationNumberTermination(50)
};
ga.Start();
Assert.IsNotNull(ga.BestChromosome.Fitness);
TestContext.WriteLine($"Best fitness is {ga.BestChromosome.Fitness} and source looks like: \n {ga.BestChromosome}");
}
public void Cross_ParentWithNoOrderedGenes_Exception()
{
var target = new OrderedCrossover();
var chromosome1 = MockRepository.GenerateStub <ChromosomeBase>(10);
chromosome1.ReplaceGenes(0, new Gene[] {
new Gene(8),
new Gene(4),
new Gene(7),
new Gene(3),
new Gene(6),
new Gene(2),
new Gene(5),
new Gene(1),
new Gene(9),
new Gene(0)
});
chromosome1.Expect(c => c.CreateNew()).Return(MockRepository.GenerateStub <ChromosomeBase>(10));
var chromosome2 = MockRepository.GenerateStub <ChromosomeBase>(10);
chromosome2.ReplaceGenes(0, new Gene[]
{
new Gene(0),
new Gene(1),
new Gene(2),
new Gene(3),
new Gene(5),
new Gene(5),
new Gene(6),
new Gene(7),
new Gene(8),
new Gene(9),
});
chromosome2.Expect(c => c.CreateNew()).Return(MockRepository.GenerateStub <ChromosomeBase>(10));
ExceptionAssert.IsThrowing(new CrossoverException(target, "The Ordered Crossover (OX1) can be only used with ordered chromosomes. The specified chromosome has repeated genes."), () =>
{
target.Cross(new List <IChromosome>()
{
chromosome1, chromosome2
});
});
}
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 Cross_ParentWithNoOrderedGenes_Exception()
{
var target = new OrderedCrossover();
var chromosome1 = Substitute.For <ChromosomeBase>(10);
chromosome1.ReplaceGenes(0, new Gene[] {
new Gene(8),
new Gene(4),
new Gene(7),
new Gene(3),
new Gene(6),
new Gene(2),
new Gene(5),
new Gene(1),
new Gene(9),
new Gene(0)
});
chromosome1.CreateNew().Returns(Substitute.For <ChromosomeBase>(10));
var chromosome2 = Substitute.For <ChromosomeBase>(10);
chromosome2.ReplaceGenes(0, new Gene[]
{
new Gene(0),
new Gene(1),
new Gene(2),
new Gene(3),
new Gene(5),
new Gene(5),
new Gene(6),
new Gene(7),
new Gene(8),
new Gene(9),
});
chromosome2.CreateNew().Returns(Substitute.For <ChromosomeBase>(10));
Assert.Catch <CrossoverException>(() =>
{
target.Cross(new List <IChromosome>()
{
chromosome1, chromosome2
});
}, "The Ordered Crossover (OX1) can be only used with ordered chromosomes. The specified chromosome has repeated genes.");
}
protected void RunGeneticAlgorithm()
{
try
{
var referencePoint = new ReferencePoint(new GeoPoint3D(_view.StartLongitude, _view.StartLatitude), new Point3D(0, 0));
TrajectoryFitness.ReferencePoint = referencePoint;
TrajectoryFitness.GeoEndPoint = new GeoPoint3D(_view.EndLongitude, _view.EndLatitude);
TrajectoryFitness.OptimiseFuel = !_view.MinimiseNoise;
TrajectoryFitness.TakeoffHeading = _view.TakeoffHeading;
TrajectoryFitness.TakeoffSpeed = _view.TakeoffSpeed;
TrajectoryFitness.trajectories = null;
TrajectoryFitness.Best = int.MinValue;
TrajectoryFitness.PopulationData = new PopulationData2("population.dat", referencePoint);
var selection = new EliteSelection();
var crossover = new OrderedCrossover();
var mutation = new ReverseSequenceMutation();
var fitness = new TrajectoryFitness();
var chromosome = new TrajectoryChromosome(TrajectoryChromosome.ChromosomeLength(_view.NumberOfSegments), _view.NumberOfSegments);
var population = new Population(_view.PopulationSize, _view.PopulationSize, chromosome);
ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
var executor = new SmartThreadPoolTaskExecutor();
executor.MinThreads = 1;
executor.MaxThreads = 1;
ga.TaskExecutor = executor;
ga.Termination = new GenerationNumberTermination(_view.NumberOfGenerations);
startTime = DateTime.Now;
ga.TerminationReached += OptimisationCompleted;
ga.GenerationRan += UpdatePercentage;
ga.Start();
}
catch (ThreadAbortException)
{
// ignore it
}
catch (Exception ex)
{
MessageBox.Show(ex.Message);
}
}
static void Main(string[] args)
{
Console.WriteLine("Hello World!");
var selection = new GeneticSharp.Domain.Selections.RouletteWheelSelection();
var crossover = new OrderedCrossover();
var mutation = new ReverseSequenceMutation();
var fitness = new MyProblemFitness();
var chromosome = new MyProblemChromosome();
var population = new Population(50, 70, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new GenerationNumberTermination(100);
Console.WriteLine("GA running...");
ga.Start();
Console.WriteLine("Best solution found has {0} fitness.", ga.BestChromosome.Fitness);
}
protected override GeneticAlgorithm CreateGA()
{
var size = (int)Camera.main.orthographicSize - 1;
m_fitness = new TspFitness(m_numberOfCities, -size, size, -size, size);
var chromosome = new TspChromosome(m_numberOfCities);
var crossover = new OrderedCrossover();
var mutation = new ReverseSequenceMutation();
var selection = new RouletteWheelSelection();
var population = new Population(50, 100, chromosome);
var ga = new GeneticAlgorithm(population, m_fitness, selection, crossover, mutation);
ga.Termination = new TimeEvolvingTermination(System.TimeSpan.FromDays(1));
ga.TaskExecutor = new ParallelTaskExecutor
{
MinThreads = 100,
MaxThreads = 200
};
return(ga);
}
private GeneticAlgorithm CreateGA(Func <TspChromosome, Population> createPopulation = null)
{
var selection = new EliteSelection();
var crossover = new OrderedCrossover();
var mutation = new ReverseSequenceMutation();
var chromosome = new TspChromosome(_numberOfCities);
var fitness = new TspFitness(_numberOfCities, 0, 1000, 0, 1000);
var population = createPopulation == null
? new Population(_minPopulationSize, _minPopulationSize, chromosome)
: createPopulation(chromosome);
population.GenerationStrategy = new PerformanceGenerationStrategy();
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new GenerationNumberTermination(_generations)
};
return(ga);
}
public void TSPSolve(int numberOfPoints, List <Point> stationList)
{
var fitness = new TspFitness(stationList);
var chromosome = new TspChromosome(numberOfPoints);
var crossover = new OrderedCrossover();
var mutation = new ReverseSequenceMutation();
var selection = new RouletteWheelSelection();
var population = new Population(50, 100, chromosome);
m_ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
m_ga.Termination = new FitnessStagnationTermination(100);
m_ga.TaskExecutor = new ParallelTaskExecutor {
MinThreads = 100,
MaxThreads = 200
};
m_ga.GenerationRan += delegate {
var distance = ((TspChromosome)m_ga.BestChromosome).Distance;
};
m_ga.TerminationReached += delegate {
var c = m_ga.Population.CurrentGeneration.BestChromosome as TspChromosome;
var genes = c.GetGenes();
var points = ((TspFitness)m_ga.Fitness).Points;
finalPoints = new List <Point>();
for (int i = 0; i < genes.Length; i++)
{
finalPoints.Add(points[(int)genes[i].Value]);
}
tspSolved();
};
m_gaThread = new Thread(() => m_ga.Start());
m_gaThread.Start();
}
/// <summary>
/// GeneticSharp TSP Console Application template.
/// <see href="https://github.com/giacomelli/GeneticSharp"/>
/// </summary>
static void Main(string[] args)
{
var selection = new EliteSelection();
var crossover = new OrderedCrossover();
var mutation = new TworsMutation();
var fitness = new TspFitness(20, 0, 1000, 0, 1000);
var chromosome = new TspChromosome(fitness.Cities.Count);
var population = new Population(50, 70, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new FitnessStagnationTermination(100)
};
ga.GenerationRan += (s, e) =>
{
Console.Clear();
Console.WriteLine($"Generation: {ga.GenerationsNumber}");
var c = ga.BestChromosome as TspChromosome;
Console.WriteLine("Cities: {0:n0}", c.Length);
Console.WriteLine("Distance: {0:n2}", c.Distance);
var cities = c.GetGenes().Select(g => g.Value.ToString()).ToArray();
Console.WriteLine("City tour: {0}", string.Join(", ", cities));
};
Console.WriteLine("GA running...");
ga.Start();
Console.WriteLine();
Console.WriteLine($"Elapsed time: {ga.TimeEvolving}");
Console.WriteLine("Done.");
Console.ReadKey();
}
public void NextGeneration(ISelection selectionScheme)
{
var selected = selectionScheme.Select(Chromosomes, 5).OrderByDescending(s => s.Cost).ToList();
Chromosomes.Remove(selected[0]);
Chromosomes.Remove(selected[1]);
// fill the remaining empty space with children generated from selected chromosomes
Chromosome parent1 = selected[3];
Chromosome parent2 = selected[4];
Chromosome child1 = OrderedCrossover.MakeChild(parent1, parent2);
Chromosome child2 = OrderedCrossover.MakeChild(parent2, parent1);
if (ThreadSafeRandom.CurrentThreadRandom.Next(100) < 3)
{
child1.Mutate();
}
if (ThreadSafeRandom.CurrentThreadRandom.Next(100) < 3)
{
child2.Mutate();
}
Chromosomes.Add(child1);
Chromosomes.Add(child2);
double currentTour = Chromosomes.Where(c => c.Cost > 0).Min(c => c.Cost);
if (currentTour < BestTour)
{
BestTour = currentTour;
Console.WriteLine("Generation " + currentGeneration + " Best: " + BestTour);
}
currentGeneration++;
}
private void StartEvolution()
{
SetupSourceColorMatrix();
if (currentDrawing == null)
{
currentDrawing = GetNewInitializedDrawing();
}
lastSelected = 0;
var selection = new EliteSelection();
// var selection = new CustomSelection(20);
var crossover = new OrderedCrossover();
// var crossover = new CycleCrossover();
// var crossover = new CutAndSpliceCrossover();
// var crossover = new OnePointCrossover(2);
var mutation = new CustomMutation();
// var mutation = new UniformMutation(true);
var fitness = new DrawingFitness(sourceColors);
var chromosome = new PolygonChromosome();
var population = new Population(10, 100, chromosome);
_ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
_ga.Termination = new TimeEvolvingTermination(TimeSpan.FromHours(4));
//
_ga.GenerationRan += (sender, args) =>
{
generation++;
var best = _ga.Population.CurrentGeneration.Chromosomes.OrderBy(c => c.Fitness).First() as PolygonChromosome;
var drawing = new DnaDrawing(best);
if (best.Fitness.Value <= errorLevel)
{
errorLevel = best.Fitness.Value;
lock (currentDrawing)
{
currentDrawing = drawing;
}
selected++;
}
};
_ga.Stopped += (sender, args) =>
{
Stop();
};
_ga.Start();
// while (isRunning)
// {
// DnaDrawing newDrawing;
// lock (currentDrawing)
// {
// newDrawing = currentDrawing.Clone();
// }
// newDrawing.Mutate();
//
// if (newDrawing.IsDirty)
// {
// generation++;
//
// double newErrorLevel = FitnessCalculator.GetDrawingFitness(
// newDrawing,
// sourceColors);
//
// if (newErrorLevel <= errorLevel)
// {
// selected++;
// lock (currentDrawing)
// {
// currentDrawing = newDrawing;
// }
// errorLevel = newErrorLevel;
// }
// }
// //else, discard new drawing
// }
}
private void EvolveGeneticStrategyButton_Click(object sender, RoutedEventArgs e)
{
OutputTextBlock.Text = "Evolving...";
Task.Run(() =>
{
var chromosome = new BlackjackChromosome();
var fitness = new BlackjackFitness();
var population = new Population(Settings.GeneticSettings.MinPopulationSize, Settings.GeneticSettings.MaxPopulationSize, chromosome);
ISelection selection;
switch (Settings.GeneticSettings.SelectionType)
{
case SelectionType.Elite:
selection = new EliteSelection();
break;
case SelectionType.RouletteWheel:
selection = new RouletteWheelSelection();
break;
case SelectionType.StochasticUniversalSampling:
selection = new StochasticUniversalSamplingSelection();
break;
case SelectionType.Tournament:
selection = new TournamentSelection(Settings.GeneticSettings.TournamentSize);
break;
default:
throw new InvalidOperationException();
}
ICrossover crossover;
switch (Settings.GeneticSettings.CrossoverType)
{
case CrossoverType.AlternatingPosition:
crossover = new AlternatingPositionCrossover();
break;
case CrossoverType.CutAndSplice:
crossover = new CutAndSpliceCrossover();
break;
case CrossoverType.Cycle:
crossover = new CycleCrossover();
break;
case CrossoverType.OnePoint:
crossover = new OnePointCrossover();
break;
case CrossoverType.TwoPoint:
crossover = new TwoPointCrossover();
break;
case CrossoverType.OrderBased:
crossover = new OrderBasedCrossover();
break;
case CrossoverType.Ordered:
crossover = new OrderedCrossover();
break;
case CrossoverType.PartiallyMapped:
crossover = new PartiallyMappedCrossover();
break;
case CrossoverType.PositionBased:
crossover = new PositionBasedCrossover();
break;
case CrossoverType.ThreeParent:
crossover = new ThreeParentCrossover();
break;
case CrossoverType.Uniform:
crossover = new UniformCrossover(Settings.Current.GeneticSettings.MixProbability);
break;
case CrossoverType.VotingRecombination:
crossover = new VotingRecombinationCrossover();
break;
default:
throw new InvalidOperationException();
}
var mutation = new UniformMutation();
var termination = new FitnessStagnationTermination(Settings.Current.GeneticSettings.NumStagnantGenerations);
var taskExecutor = new ParallelTaskExecutor();
var ga = new GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation);
ga.Termination = termination;
ga.TaskExecutor = taskExecutor;
ga.MutationProbability = Settings.GeneticSettings.MutationProbability;
ga.CrossoverProbability = Settings.GeneticSettings.CrossoverProbability;
var latestFitness = double.MinValue;
ga.GenerationRan += (s, o) =>
{
geneticStrategy = (IStrategy)ga.BestChromosome;
var generationNumber = ga.GenerationsNumber;
var bestFitness = ga.BestChromosome.Fitness.Value;
var avgFitness = ga.Population.CurrentGeneration.Chromosomes.Average(c => c.Fitness.Value);
Dispatcher.Invoke(() =>
{
if (generationNumber == 1)
{
OutputTextBlock.Text = string.Empty;
}
OutputTextBlock.Text = $"Gen: {generationNumber}\tFit: {bestFitness}\tAvg: {avgFitness.ToString("0")}\n" + OutputTextBlock.Text;
if (bestFitness != latestFitness)
{
latestFitness = bestFitness;
var savedImageName = Settings.Current.GeneticSettings.SaveImagePerGeneration ? "gen" + generationNumber : null;
StrategyViewer.Draw(GeneticStrategyCanvas, geneticStrategy, $"Best from generation {generationNumber}", savedImageName);
}
}, DispatcherPriority.Background);
};
ga.TerminationReached += (s, o) =>
{
Dispatcher.Invoke(() =>
{
OutputTextBlock.Text = "Termination reached.\n" + OutputTextBlock.Text;
TestGeneticStrategyButton.IsEnabled = true;
}, DispatcherPriority.Background);
};
ga.Start();
});
}
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 void Cross_ParentsWith10Genes_Cross()
{
var target = new OrderedCrossover();
// 8 4 7 3 6 2 5 1 9 0
var chromosome1 = MockRepository.GenerateStub <ChromosomeBase>(10);
chromosome1.ReplaceGenes(0, new Gene[] {
new Gene(8),
new Gene(4),
new Gene(7),
new Gene(3),
new Gene(6),
new Gene(2),
new Gene(5),
new Gene(1),
new Gene(9),
new Gene(0)
});
chromosome1.Expect(c => c.CreateNew()).Return(MockRepository.GenerateStub <ChromosomeBase>(10));
// 0 1 2 3 4 5 6 7 8 9
var chromosome2 = MockRepository.GenerateStub <ChromosomeBase>(10);
chromosome2.ReplaceGenes(0, new Gene[]
{
new Gene(0),
new Gene(1),
new Gene(2),
new Gene(3),
new Gene(4),
new Gene(5),
new Gene(6),
new Gene(7),
new Gene(8),
new Gene(9),
});
chromosome2.Expect(c => c.CreateNew()).Return(MockRepository.GenerateStub <ChromosomeBase>(10));
// Child one: 0 4 7 3 6 2 5 1 8 9
// Child two: 8 2 1 3 4 5 6 7 9 0
var rnd = MockRepository.GenerateMock <IRandomization>();
rnd.Expect(r => r.GetUniqueInts(2, 0, 10)).Return(new int[] { 7, 3 });
RandomizationProvider.Current = rnd;
IList <IChromosome> actual = null;;
TimeAssert.LessThan(30, () => {
actual = target.Cross(new List <IChromosome> ()
{
chromosome1, chromosome2
});
});
Assert.AreEqual(2, actual.Count);
Assert.AreEqual(10, actual[0].Length);
Assert.AreEqual(10, actual[1].Length);
Assert.AreEqual(10, actual[0].GetGenes().Distinct().Count());
Assert.AreEqual(10, actual[1].GetGenes().Distinct().Count());
Assert.AreEqual(0, actual[0].GetGene(0).Value);
Assert.AreEqual(4, actual[0].GetGene(1).Value);
Assert.AreEqual(7, actual[0].GetGene(2).Value);
Assert.AreEqual(3, actual[0].GetGene(3).Value);
Assert.AreEqual(6, actual[0].GetGene(4).Value);
Assert.AreEqual(2, actual[0].GetGene(5).Value);
Assert.AreEqual(5, actual[0].GetGene(6).Value);
Assert.AreEqual(1, actual[0].GetGene(7).Value);
Assert.AreEqual(8, actual[0].GetGene(8).Value);
Assert.AreEqual(9, actual[0].GetGene(9).Value);
Assert.AreEqual(8, actual[1].GetGene(0).Value);
Assert.AreEqual(2, actual[1].GetGene(1).Value);
Assert.AreEqual(1, actual[1].GetGene(2).Value);
Assert.AreEqual(3, actual[1].GetGene(3).Value);
Assert.AreEqual(4, actual[1].GetGene(4).Value);
Assert.AreEqual(5, actual[1].GetGene(5).Value);
Assert.AreEqual(6, actual[1].GetGene(6).Value);
Assert.AreEqual(7, actual[1].GetGene(7).Value);
Assert.AreEqual(9, actual[1].GetGene(8).Value);
Assert.AreEqual(0, actual[1].GetGene(9).Value);
}
public void Cross_ParentsWith10Genes_Cross()
{
var target = new OrderedCrossover();
// 8 4 7 3 6 2 5 1 9 0
var chromosome1 = Substitute.For <ChromosomeBase>(10);
chromosome1.ReplaceGenes(0, new Gene[] {
new Gene(8),
new Gene(4),
new Gene(7),
new Gene(3),
new Gene(6),
new Gene(2),
new Gene(5),
new Gene(1),
new Gene(9),
new Gene(0)
});
chromosome1.CreateNew().Returns(Substitute.For <ChromosomeBase>(10));
// 0 1 2 3 4 5 6 7 8 9
var chromosome2 = Substitute.For <ChromosomeBase>(10);
chromosome2.ReplaceGenes(0, new Gene[]
{
new Gene(0),
new Gene(1),
new Gene(2),
new Gene(3),
new Gene(4),
new Gene(5),
new Gene(6),
new Gene(7),
new Gene(8),
new Gene(9),
});
chromosome2.CreateNew().Returns(Substitute.For <ChromosomeBase>(10));
// Child one: 0 4 7 3 6 2 5 1 8 9
// Child two: 8 2 1 3 4 5 6 7 9 0
var rnd = Substitute.For <IRandomization>();
rnd.GetUniqueInts(2, 0, 10).Returns(new int[] { 7, 3 });
RandomizationProvider.Current = rnd;
var actual = target.Cross(new List <IChromosome>()
{
chromosome1, chromosome2
});
Assert.AreEqual(2, actual.Count);
Assert.AreEqual(10, actual[0].Length);
Assert.AreEqual(10, actual[1].Length);
Assert.AreEqual(10, actual[0].GetGenes().Distinct().Count());
Assert.AreEqual(10, actual[1].GetGenes().Distinct().Count());
Assert.AreEqual(0, actual[0].GetGene(0).Value);
Assert.AreEqual(4, actual[0].GetGene(1).Value);
Assert.AreEqual(7, actual[0].GetGene(2).Value);
Assert.AreEqual(3, actual[0].GetGene(3).Value);
Assert.AreEqual(6, actual[0].GetGene(4).Value);
Assert.AreEqual(2, actual[0].GetGene(5).Value);
Assert.AreEqual(5, actual[0].GetGene(6).Value);
Assert.AreEqual(1, actual[0].GetGene(7).Value);
Assert.AreEqual(8, actual[0].GetGene(8).Value);
Assert.AreEqual(9, actual[0].GetGene(9).Value);
Assert.AreEqual(8, actual[1].GetGene(0).Value);
Assert.AreEqual(2, actual[1].GetGene(1).Value);
Assert.AreEqual(1, actual[1].GetGene(2).Value);
Assert.AreEqual(3, actual[1].GetGene(3).Value);
Assert.AreEqual(4, actual[1].GetGene(4).Value);
Assert.AreEqual(5, actual[1].GetGene(5).Value);
Assert.AreEqual(6, actual[1].GetGene(6).Value);
Assert.AreEqual(7, actual[1].GetGene(7).Value);
Assert.AreEqual(9, actual[1].GetGene(8).Value);
Assert.AreEqual(0, actual[1].GetGene(9).Value);
}
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);
}
static void Main(string[] args)
{
var selection = new EliteSelection();
var crossover = new OrderedCrossover();
}
public IList <IChromosome> OrderedCrossover()
{
var target = new OrderedCrossover();
return(target.Cross(CreateTwoParents()));
}
