public void Evolve_ManyGenerations_Fast()
{
var selection = new EliteSelection();
var crossover = new ThreeParentCrossover();
var mutation = new UniformMutation(true);
var fitness = new FunctionBuilderFitness(
new FunctionBuilderInput(
new double[] { 1, 2 },
3)
,
new FunctionBuilderInput(
new double[] { 2, 3 },
5)
);
var chromosome = new FunctionBuilderChromosome(fitness.AvailableOperations, 3);
var population = new Population(100, 200, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new OrTermination(new FitnessThresholdTermination(0), new TimeEvolvingTermination(TimeSpan.FromSeconds(15)));
ga.Start();
var bestChromosome = ga.BestChromosome as FunctionBuilderChromosome;
Assert.AreEqual(0.0, bestChromosome.Fitness.Value);
var actual = fitness.GetFunctionResult(
bestChromosome.BuildFunction(),
new FunctionBuilderInput(new double[] { 3, 4 }, 7)
);
Assert.AreEqual(7, actual);
}
public void Cross_ThreeParents_OneChildren()
{
var chromosome1 = MockRepository.GenerateStub <ChromosomeBase>(4);
chromosome1.ReplaceGenes(0, new Gene[]
{
new Gene(1),
new Gene(2),
new Gene(3),
new Gene(4),
});
chromosome1.Expect(c => c.CreateNew()).Return(MockRepository.GenerateStub <ChromosomeBase>(4));
var chromosome2 = MockRepository.GenerateStub <ChromosomeBase>(4);
chromosome2.ReplaceGenes(0, new Gene[]
{
new Gene(1),
new Gene(5),
new Gene(6),
new Gene(4)
});
chromosome2.Expect(c => c.CreateNew()).Return(MockRepository.GenerateStub <ChromosomeBase>(4));
var chromosome3 = MockRepository.GenerateStub <ChromosomeBase>(4);
chromosome3.ReplaceGenes(0, new Gene[]
{
new Gene(10),
new Gene(11),
new Gene(12),
new Gene(13)
});
chromosome3.Expect(c => c.CreateNew()).Return(MockRepository.GenerateStub <ChromosomeBase>(4));
var parents = new List <IChromosome>()
{
chromosome1, chromosome2, chromosome3
};
var target = new ThreeParentCrossover();
var actual = target.Cross(parents);
Assert.AreEqual(1, actual.Count);
Assert.AreEqual(4, actual[0].Length);
Assert.AreEqual(1, actual[0].GetGene(0).Value);
Assert.AreEqual(11, actual[0].GetGene(1).Value);
Assert.AreEqual(12, actual[0].GetGene(2).Value);
Assert.AreEqual(4, actual[0].GetGene(3).Value);
}
public void Cross_ThreeParents_OneChildren()
{
var chromosome1 = Substitute.For <ChromosomeBase>(4);
chromosome1.ReplaceGenes(0, new Gene[]
{
new Gene(1),
new Gene(2),
new Gene(3),
new Gene(4),
});
chromosome1.CreateNew().Returns(Substitute.For <ChromosomeBase>(4));
var chromosome2 = Substitute.For <ChromosomeBase>(4);
chromosome2.ReplaceGenes(0, new Gene[]
{
new Gene(1),
new Gene(5),
new Gene(6),
new Gene(4)
});
chromosome2.CreateNew().Returns(Substitute.For <ChromosomeBase>(4));
var chromosome3 = Substitute.For <ChromosomeBase>(4);
chromosome3.ReplaceGenes(0, new Gene[]
{
new Gene(10),
new Gene(11),
new Gene(12),
new Gene(13)
});
chromosome3.CreateNew().Returns(Substitute.For <ChromosomeBase>(4));
var parents = new List <IChromosome>()
{
chromosome1, chromosome2, chromosome3
};
var target = new ThreeParentCrossover();
var actual = target.Cross(parents);
Assert.AreEqual(1, actual.Count);
Assert.AreEqual(4, actual[0].Length);
Assert.AreEqual(1, actual[0].GetGene(0).Value);
Assert.AreEqual(11, actual[0].GetGene(1).Value);
Assert.AreEqual(12, actual[0].GetGene(2).Value);
Assert.AreEqual(4, actual[0].GetGene(3).Value);
}
static void Main(string[] args)
{
// Problem: Simple addition
// 1 + 1 = 2
// 2 + 2 = 4
// 3 + 2 = 5
var myInputs = new List <FunctionBuilderInput>
{
new FunctionBuilderInput(new List <double> {
1, 1
}, 2),
new FunctionBuilderInput(new List <double> {
2, 2
}, 4),
new FunctionBuilderInput(new List <double> {
3, 2
}, 5)
};
var myFitness = new FunctionBuilderFitness(myInputs.ToArray());
var myChromosome = new FunctionBuilderChromosome(myFitness.AvailableOperations, 5);
var selection = new EliteSelection();
var crossover = new ThreeParentCrossover();
var mutation = new UniformMutation(true);
var fitness = myFitness;
var chromosome = myChromosome;
var population = new Population(100, 200, chromosome)
{
GenerationStrategy = new PerformanceGenerationStrategy()
};
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new FitnessThresholdTermination(0)
};
ga.GenerationRan += delegate
{
Console.Clear();
var bestChromosome = ga.Population.BestChromosome;
Console.WriteLine("Generations: {0}", ga.Population.GenerationsNumber);
Console.WriteLine("Fitness: {0,10}", bestChromosome.Fitness);
Console.WriteLine("Time: {0}", ga.TimeEvolving);
Console.WriteLine("Speed (gen/sec): {0:0.0000}", ga.Population.GenerationsNumber / ga.TimeEvolving.TotalSeconds);
var best = bestChromosome as FunctionBuilderChromosome;
Console.WriteLine("Function: {0}", best.BuildFunction());
};
ga.Start();
Console.WriteLine("Evolved.");
Console.ReadKey();
}
public void StartGeneticAlgo(string target)
{
var selection = new EliteSelection();
var crossover = new ThreeParentCrossover();
var mutation = new ReverseSequenceMutation();
var fitness = new MyFitness {
Target = target
};
var chromosome = new Chromosome(target);
var population = new Population(500, 1000, chromosome);
_stopwatch = Stopwatch.StartNew();
_ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new GenerationNumberTermination(300),
MutationProbability = float.MaxValue
};
_timer = new Timer(new TimerCallback(_ =>
{
_ga.Termination = new GenerationNumberTermination(_ga.GenerationsNumber + 1);
if (_ga.GenerationsNumber > 0)
{
_ga.Resume();
}
else
{
_ga.Start();
}
var teste = string.Empty;
for (var i = 0; i < _ga.BestChromosome.GetGenes().Length; i++)
{
teste += _ga.BestChromosome.GetGenes()[i].Value;
if (teste == target)
{
Stop();
_stopwatch.Stop();
}
}
StateHasChanged?.Invoke();
}), null, 0, 1);
}
public void Start_ThreeParentCrossover_KeepsMinSizePopulation()
{
var selection = new EliteSelection();
var crossover = new ThreeParentCrossover();
var mutation = new UniformMutation();
var chromosome = new ChromosomeStub();
var target = new GeneticAlgorithm(new Population(100, 199, chromosome),
new FitnessStub()
{
SupportsParallel = false
}, selection, crossover, mutation);
target.Termination = new GenerationNumberTermination(100);
target.Start();
Assert.AreEqual(100, target.Population.Generations.Count);
Assert.IsTrue(target.Population.Generations.All(g => g.Chromosomes.Count >= 100));
}
public OutputData DoIt(InputData inputData)
{
var selection = new EliteSelection();
var crossover = new ThreeParentCrossover();;; //OnePointCrossover
var mutation = new ReverseSequenceMutation(); //DisplacementMutation ReverseSequenceMutation
var fitness = new MyProblemFitness(inputData);
var chromosome = new MyProblemChromosome(inputData.F, inputData.N);
var population = new Population(100, 1000, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new FitnessStagnationTermination(10000);
ga.Population.GenerationStrategy = new TrackingGenerationStrategy();
Console.WriteLine("GA running...");
double currentFitness = 0;
ga.GenerationRan += delegate(object obj, EventArgs eventArgs)
{
var outputx = new MyProblemFitness(inputData);
var re = outputx.Evaluate(((GeneticAlgorithm)obj).BestChromosome);
if (re != currentFitness)
{
currentFitness = re;
Console.WriteLine("Fitness..." + re);
}
};
ga.Start();
var x = ga.BestChromosome;
var output = new MyProblemFitness(inputData);
var res = output.Evaluate(ga.BestChromosome);
Console.WriteLine("Done...fitness..." + res);
return(output.OutputData);
}
public void functionOptimizationWithGeneticsharp()
{
/* example from
* http://diegogiacomelli.com.br/function-optimization-with-geneticsharp/
*/
float maxWidth = 998f;
float maxHeight = 680f;
var chromosome = new FloatingPointChromosome(
new double[] { 0, 0, 0, 0 },
new double[] { maxWidth, maxHeight, maxWidth, maxHeight },
new int[] { 12, 12, 12, 12 },
new int[] { 0, 0, 0, 0 });
var population = new Population(50, 100, chromosome);
var fitness = new FuncFitness((c) =>
{
var fc = c as FloatingPointChromosome;
var values = fc.ToFloatingPoints();
var x1 = values[0];
var y1 = values[1];
var x2 = values[2];
var y2 = values[3];
return(Math.Sqrt(Math.Pow(x2 - x1, 2) + Math.Pow(y2 - y1, 2)));
});
var selection = new RouletteWheelSelection();
var crossover = new ThreeParentCrossover();
var mutation = new PartialShuffleMutation();
var termination = new FitnessStagnationTermination(100);
var ga = new GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation);
ga.Termination = termination;
Console.WriteLine("Generation: (x1, y1), (x2, y2) = distance");
var latestFitness = 0.0;
ga.GenerationRan += (sender, e) =>
{
var bestChromosome = ga.BestChromosome as FloatingPointChromosome;
var bestFitness = bestChromosome.Fitness.Value;
if (bestFitness != latestFitness)
{
latestFitness = bestFitness;
var phenotype = bestChromosome.ToFloatingPoints();
var s = string.Format("Generation {0,2}: ({1},{2}),({3},{4}) = {5}",
ga.GenerationsNumber,
phenotype[0],
phenotype[1],
phenotype[2],
phenotype[3],
bestFitness);
Console.WriteLine(
s
);
}
};
ga.Start();
}
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));
}
private void submitButton_Click(object sender, RoutedEventArgs ev)
{
resultBox.Text = "";
if (string.IsNullOrEmpty(textBox.Text))
{
resultBox.Text = "Wybierz plik z danymi...";
return;
}
string[] lines = File.ReadAllLines(textBox.Text);
int edgesNumber = lines.Count();
List <Edge> edges = new List <Edge>();
int edgeIndex = 0;
string stringSeparator = " ";
foreach (string l in lines)
{
int[] values = l.Split(stringSeparator.ToCharArray(), StringSplitOptions.None).Select(s => int.Parse(s)).ToArray();
// Krawędź może być przechodzona w obu kierunkach
edges.Add(new Edge(values[0], values[1], values[2], edgeIndex));
// Ddodawana jest także w odwróconej wersji
edges.Add(new Edge(values[1], values[0], values[2], edgeIndex));
//Krawędź i jej odwrócona wersja mają ten sam indeks(dla łatwiejszego odnajdowania)
edgeIndex++;
}
EliteSelection selection = new EliteSelection();
ThreeParentCrossover crossover = new ThreeParentCrossover();
TworsMutation mutation = new TworsMutation();
Fitness fitness = new Fitness(edges);
Chromosome chromosome = new Chromosome(4 * edgesNumber, edges);
Population population = new Population(200, 400, chromosome);
GeneticAlgorithm ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new GenerationNumberTermination(400)
};
Stopwatch timer = new Stopwatch();
timer.Start();
ga.Start();
timer.Stop();
Chromosome bestChromosome = ga.BestChromosome as Chromosome;
int currentEdgeIndex = int.Parse(bestChromosome.GetGene(0).Value.ToString());
Edge currentEdge = edges[currentEdgeIndex];
int startVertex = currentEdge.VertexA;
int totalCost = currentEdge.Cost;
string verticesSequence = currentEdge.VertexA + "-" + currentEdge.VertexB;
resultBox.Text += $"Funkcja dopasowania najlepszego rozwiązania wynosi: {bestChromosome.Fitness}\n";
for (int i = 1; i < bestChromosome.Length; i++)
{
currentEdgeIndex = int.Parse(bestChromosome.GetGene(i).Value.ToString());
currentEdge = edges[currentEdgeIndex];
currentEdge.Visited = true;
edges.SingleOrDefault(e => e.VertexA == currentEdge.VertexB && e.VertexB == currentEdge.VertexA).Visited = true;
totalCost += currentEdge.Cost;
verticesSequence += "-" + currentEdge.VertexB;
if (Fitness.AllEdgesVisited(edges))
{
if (currentEdge.VertexB == startVertex)
{
break;
}
Edge possibleEdge = edges.SingleOrDefault(e => e.VertexA == currentEdge.VertexB && e.VertexB == startVertex);
if (possibleEdge != null)
{
totalCost += possibleEdge.Cost;
verticesSequence += "-" + possibleEdge.VertexB;
break;
}
}
}
resultBox.Text += $"Ścieżka: {verticesSequence}\n";
resultBox.Text += $"Koszt najlepszego rozwiązania: {totalCost}\n";
resultBox.Text += $"Czas wykonania: {timer.Elapsed.ToString(@"hh\:mm\:ss\.ff")}\n";
}
static void Main(string[] args)
{
// grafy o różnej liczbie wierzchołków h7 h9 h20 h23 h25 h47
string loc = Directory.GetCurrentDirectory() + "\\Grafy\\h7.txt";
int[,] data = LoadData.FromFile(loc);
drogi = new List <Droga>();
for (int i = 0; i < data.GetLength(0); i++)
{
Droga nowaDroga = new Droga(data[i, 0], data[i, 1], data[i, 2]);
drogi.Add(nowaDroga);
Droga powrotnaDroga = new Droga(data[i, 1], data[i, 0], data[i, 2]);
drogi.Add(powrotnaDroga);
}
System.Console.WriteLine("Lista drog :");
foreach (Droga droga in drogi)
{
System.Console.WriteLine(droga.pktPoczatkowy + " " + droga.pktNastepny + " " + droga.koszt);
}
Console.WriteLine("liczba dróg : " + drogi.Count);
Console.WriteLine();
var selection = new EliteSelection();
var crossover = new ThreeParentCrossover(); //dwóch rodziców dwojka dzieci TwoPointCrossover();//
var mutation = new TworsMutation(); // zmiana pozycji dwóch genów bybranych losowo
var fitness = new PostmanFitness();
var chromosome = new PostmanChromosome(3 * drogi.Count);
var population = new Population(30, 30, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.CrossoverProbability = 0.90F;
ga.MutationProbability = 0.05F;
ga.Termination = new GenerationNumberTermination(800);
Console.WriteLine("Prawdopodobieństwo krzyżowania: " + ga.CrossoverProbability);
Console.WriteLine("Prawdopodobieństwo mutacji: " + ga.MutationProbability);
Stopwatch timer = new Stopwatch();
timer.Start();
Console.WriteLine("Obliczanie...");
ga.Start();
Console.WriteLine("Koniec obliczeń");
timer.Stop();
bool pierwszy = true;
int miastoStartoweIndex = Convert.ToInt32(ga.BestChromosome.GetGenes()[0].Value);
int miastoStartowe = drogi[miastoStartoweIndex].pktPoczatkowy;
int kosztCalkowity = 0;
foreach (Gene gene in ga.BestChromosome.GetGenes())
{
int index = Convert.ToInt32(gene.Value);
kosztCalkowity += drogi[index].koszt;
if (PostmanFitness.czyKazdaDrogaZostalaPokonana(drogi) && drogi[index].pktNastepny == miastoStartowe)
{
Console.Write("-" + drogi[index].pktNastepny.ToString());
break;
}
else
{
if (pierwszy)
{
Console.Write(drogi[index].pktPoczatkowy.ToString() + "-" + drogi[index].pktNastepny.ToString());
pierwszy = false;
}
else
{
Console.Write("-" + drogi[index].pktNastepny.ToString());
}
drogi[index].czyDotarl = true;
Droga drogaPowrotna = drogi.Find(a => (a.pktPoczatkowy.ToString() + "-" + a.pktNastepny.ToString()).Equals(drogi[index].pktNastepny.ToString() + "-" + drogi[index].pktPoczatkowy.ToString()));
drogaPowrotna.czyDotarl = true;
}
}
TimeSpan ts = timer.Elapsed;
string elapsedTime = String.Format("{0:00}:{1:00}:{2:00}.{3:00}", ts.Hours, ts.Minutes, ts.Seconds, ts.Milliseconds / 10);
Console.WriteLine();
Console.WriteLine("Czas obliczeń : {0}", elapsedTime);
Console.WriteLine("Całkowity koszt przebytej trasy : {0}", kosztCalkowity);
Console.ReadKey();
}
static void Main(string[] args)
{
#region Loading data from File
string location = Directory.GetCurrentDirectory() + "\\Data\\graf4_kod.txt";
int[,] data = LoadDataFromFile(location);
roads = new List <Road>();
for (int i = 0; i < data.GetLength(0); i++)
{
Road newRoad = new Road(data[i, 0], data[i, 1], data[i, 2]); // one direction cost
roads.Add(newRoad);
Road reverseRoad = new Road(data[i, 1], data[i, 0], data[i, 3]); // read reverse road
roads.Add(reverseRoad);
}
#endregion
var selection = new EliteSelection();
var crossover = new ThreeParentCrossover();
var mutation = new TworsMutation();
var fitness = new CPFitness();
var chromosome = new CPChromosome(3 * roads.Count);
var population = new Population(200, 400, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new GenerationNumberTermination(400);
Stopwatch timer = new Stopwatch();
timer.Start();
Console.WriteLine("GA running...");
ga.Start();
Console.WriteLine();
timer.Stop();
bool first = true;
int startCityIndex = Convert.ToInt32(ga.BestChromosome.GetGenes()[0].Value, CultureInfo.InvariantCulture);
int startCity = roads[startCityIndex].cityFrom;
int totalCost = 0;
foreach (Gene gene in ga.BestChromosome.GetGenes())
{
int ind = Convert.ToInt32(gene.Value, CultureInfo.InvariantCulture);
totalCost += roads[ind].cost;
if (CPFitness.everyRoadIsTraveled(roads) && roads[ind].cityTo == startCity)
{
Console.Write("-" + roads[ind].cityTo.ToString());
break;
}
else
{
if (first)
{
Console.Write(roads[ind].cityFrom.ToString() + "-" + roads[ind].cityTo.ToString());
first = false;
}
else
{
Console.Write("-" + roads[ind].cityTo.ToString());
}
roads[ind].isTravelled = true;
Road returnRoad = roads.Find(e => e.index.Equals(roads[ind].cityTo.ToString() + "-" + roads[ind].cityFrom.ToString()));
returnRoad.isTravelled = true;
}
}
TimeSpan ts = timer.Elapsed;
string elapsedTime = String.Format("{0:00}:{1:00}:{2:00}.{3:00}", ts.Hours, ts.Minutes, ts.Seconds, ts.Milliseconds / 10);
Console.WriteLine();
Console.WriteLine("Best solution found has {0} fitness.", ga.BestChromosome.Fitness);
Console.WriteLine("Best solution has total cost: {0}", totalCost);
Console.WriteLine("Time running: {0}", elapsedTime);
Console.ReadKey();
}
/// <summary>
/// Loads presetsProfile into service.
/// </summary>
/// <exception cref="T:System.InvalidOperationException">Thrown when an attempt to load presetsProfile twice is made.</exception>
public void AddGeneticAlgorithm(GADefinition definition, Guid Key)
{
var chromosome = new FloatingPointChromosome(
minValue: new double[] { 0, 0, 0, 0 },
maxValue: new double[] { _maxWidth, _maxHeight, _maxWidth, _maxHeight },
totalBits: new int[] { 20, 20, 20, 20 },
fractionDigits: new int[] { 0, 0, 0, 0 }, geneValues: _geneValues);
ICrossover gaCrossover = default;
switch (definition.Crossover)
{
case Crossovers.Uniform:
gaCrossover = new UniformCrossover(mixProbability: 0.5f);
break;
case Crossovers.OnePoint:
gaCrossover = new OnePointCrossover(swapPointIndex: 40);
break;
case Crossovers.ThreeParent:
gaCrossover = new ThreeParentCrossover();
break;
default:
throw new ArgumentOutOfRangeException(paramName: nameof(definition.Crossover),
actualValue: definition.Crossover, message: "Crossover has wrong value");
}
ISelection gaSelection = default;
switch (definition.Selection)
{
case Selections.Elite:
gaSelection = new EliteSelection();
break;
case Selections.Roulette:
gaSelection = new RouletteWheelSelection();
break;
case Selections.Tournament:
gaSelection = new TournamentSelection(
size: decimal.ToInt32(d: decimal.Multiply(definition.Population, new decimal(0.2f))));
break;
case Selections.StohasticUniversalSampling:
gaSelection = new StochasticUniversalSamplingSelection();
break;
default:
throw new ArgumentOutOfRangeException(paramName: nameof(definition.Selection),
actualValue: definition.Selection, message: "Selection has wrong value");
}
var gaMutation = new UniformMutation(true);
var gaPopulation = new Population(minSize: definition.Population,
maxSize: definition.Population, adamChromosome: chromosome);
var ga = new GeneticAlgorithm(population: gaPopulation,
fitness: new EuclideanDistanceFitness(), selection: gaSelection,
crossover: gaCrossover, mutation: gaMutation);
ga.MutationProbability = (float)definition.Mutation;
ga.GenerationRan += GeneticAlgorithmOnGenerationRan;
ga.Termination =
new FitnessStagnationTermination(expectedStagnantGenerationsNumber: 5);
_geneticAlgorithms.Add(key: Key, value: ga);
}
public IList <IChromosome> ThreeParentCrossover()
{
var target = new ThreeParentCrossover();
return(target.Cross(CreateThreeParents()));
}
private static void Main(string[] args)
{
Console.WriteLine("Podaj nazwę plkiu z danymi(bez rozszerzenia)");
string fileName = Console.ReadLine();
string[] lines = File.ReadAllLines(Directory.GetCurrentDirectory() + "\\" + fileName + ".txt");
int edgesNumber = lines.Count();
List <Edge> edges = new List <Edge>();
int edgeIndex = 0;
string stringSeparator = " ";
foreach (string l in lines)
{
int[] values = l.Split(stringSeparator.ToCharArray(), StringSplitOptions.None).Select(s => int.Parse(s)).ToArray();
// Krawędź może być przechodzona w obu kierunkach
edges.Add(new Edge(values[0], values[1], values[2], edgeIndex));
// Ddodawana jest także w odwróconej wersji
edges.Add(new Edge(values[1], values[0], values[2], edgeIndex));
//Krawędź i jej odwrócona wersja mają ten sam indeks(dla łatwiejszego odnajdowania)
edgeIndex++;
}
EliteSelection selection = new EliteSelection();
ThreeParentCrossover crossover = new ThreeParentCrossover();
TworsMutation mutation = new TworsMutation();
FitnessFunction fitness = new FitnessFunction(edges);
Chromosome chromosome = new Chromosome(4 * edgesNumber, edges);
Population population = new Population(200, 400, chromosome);
GeneticAlgorithm ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new GenerationNumberTermination(400)
};
Stopwatch timer = new Stopwatch();
timer.Start();
Console.WriteLine("START!");
ga.Start();
timer.Stop();
Chromosome bestChromosome = ga.BestChromosome as Chromosome;
int currentEdgeIndex = int.Parse(bestChromosome.GetGene(0).Value.ToString());
Edge currentEdge = edges[currentEdgeIndex];
int startVertex = currentEdge.VertexA;
int totalCost = currentEdge.Cost;
string verticesSequence = currentEdge.VertexA + "-" + currentEdge.VertexB;
Console.WriteLine("Funkcja dopasowania najlepszego rozwiązania wynosi: {0}", bestChromosome.Fitness);
for (int i = 1; i < bestChromosome.Length; i++)
{
currentEdgeIndex = int.Parse(bestChromosome.GetGene(i).Value.ToString());
currentEdge = edges[currentEdgeIndex];
currentEdge.Visited = true;
edges.SingleOrDefault(e => e.VertexA == currentEdge.VertexB && e.VertexB == currentEdge.VertexA).Visited = true;
totalCost += currentEdge.Cost;
verticesSequence += "-" + currentEdge.VertexB;
if (FitnessFunction.AllEdgesVisited(edges))
{
if (currentEdge.VertexB == startVertex)
{
break;
}
Edge possibleEdge = edges.SingleOrDefault(e => e.VertexA == currentEdge.VertexB && e.VertexB == startVertex);
if (possibleEdge != null)
{
totalCost += possibleEdge.Cost;
verticesSequence += "-" + possibleEdge.VertexB;
break;
}
}
}
Console.WriteLine("Ścieżka: {0}", verticesSequence);
Console.WriteLine("Koszt najlepszego rozwiązania: {0}", totalCost);
Console.WriteLine("Czas wykonania: {0}", timer.Elapsed.ToString(@"hh\:mm\:ss\:ff"));
Console.ReadKey();
}
