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);
}
public void Mutate_ValidChromosome_ExchangeGenes()
{
var target = new TworsMutation();
var chromosome = Substitute.For <ChromosomeBase>(4);
chromosome.ReplaceGenes(0, new Gene[]
{
new Gene(1),
new Gene(2),
new Gene(3),
new Gene(4),
});
var rnd = Substitute.For <IRandomization>();
rnd.GetUniqueInts(2, 0, 4).Returns(new int[] { 0, 2 });
RandomizationProvider.Current = rnd;
target.Mutate(chromosome, 1);
Assert.AreEqual(4, chromosome.Length);
Assert.AreEqual(3, chromosome.GetGene(0).Value);
Assert.AreEqual(2, chromosome.GetGene(1).Value);
Assert.AreEqual(1, chromosome.GetGene(2).Value);
Assert.AreEqual(4, chromosome.GetGene(3).Value);
}
public void Mutate_ValidChromosome_ExchangeGenes()
{
var target = new TworsMutation();
var chromosome = MockRepository.GenerateStub <ChromosomeBase>(4);
chromosome.ReplaceGenes(0, new Gene[]
{
new Gene(1),
new Gene(2),
new Gene(3),
new Gene(4),
});
var rnd = MockRepository.GenerateMock <IRandomization>();
rnd.Expect(r => r.GetUniqueInts(2, 0, 4)).Return(new int[] { 0, 2 });
RandomizationProvider.Current = rnd;
target.Mutate(chromosome, 1);
Assert.AreEqual(4, chromosome.Length);
Assert.AreEqual(3, chromosome.GetGene(0).Value);
Assert.AreEqual(2, chromosome.GetGene(1).Value);
Assert.AreEqual(1, chromosome.GetGene(2).Value);
Assert.AreEqual(4, chromosome.GetGene(3).Value);
rnd.VerifyAllExpectations();
chromosome.VerifyAllExpectations();
}
public void Evolve_ManyGenerations_Fast()
{
var selection = new EliteSelection();
var crossover = new UniformCrossover();
var mutation = new TworsMutation();
var chromosome = new BitmapChromosome(32, 32);
var targetBitmap = new Bitmap(32, 32);
var fitness = new BitmapEqualityFitness(targetBitmap);
var population = new Population(10, 10, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.TaskExecutor = new ParallelTaskExecutor()
{
MinThreads = 10,
MaxThreads = 20
};
ga.Termination = new GenerationNumberTermination(5);
ga.Start();
var c = ga.BestChromosome as BitmapChromosome;
Assert.IsNotNull(c);
var bitmap = c.BuildBitmap();
Assert.IsNotNull(bitmap);
Assert.AreEqual(32, bitmap.Width);
Assert.AreEqual(32, bitmap.Height);
}
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);
}
public void Mutate_NoProbality_NoExchangeGenes()
{
var target = new TworsMutation();
var chromosome = Substitute.For <ChromosomeBase>(4);
chromosome.ReplaceGenes(0, new Gene[]
{
new Gene(1),
new Gene(2),
new Gene(3),
new Gene(4),
});
var rnd = Substitute.For <IRandomization>();
rnd.GetDouble().Returns(0.1);
RandomizationProvider.Current = rnd;
target.Mutate(chromosome, 0);
Assert.AreEqual(4, chromosome.Length);
Assert.AreEqual(1, chromosome.GetGene(0).Value);
Assert.AreEqual(2, chromosome.GetGene(1).Value);
Assert.AreEqual(3, chromosome.GetGene(2).Value);
Assert.AreEqual(4, chromosome.GetGene(3).Value);
}
public IMutation TworsMutation()
{
var target = new TworsMutation();
target.Mutate(new TspChromosome(_numberOfCities), _probability);
return(target);
}
public void Evolve_CompareToSingleChromosome_Evolved()
{
int numberOfCities = 30;
var selection = new EliteSelection();
var crossover = new UniformCrossover();
var mutation = new TworsMutation();
// Given enough generations, the Multiple Chromosome should start exhibiting convergence
// we compare TSP /25 gen with 3*TSP / 500 gen
IChromosome chromosome = new TspChromosome(numberOfCities);
IFitness fitness = new TspFitness(numberOfCities, 0, 1000, 0, 1000);
var population = new Population(30, 30, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new GenerationNumberTermination(26)
};
ga.Start();
var simpleChromosomeDistance = ((TspChromosome)ga.Population.BestChromosome).Distance;
chromosome = new MultipleChromosome((i) => new TspChromosome(numberOfCities), 3);
//MultiChromosome should create 3 TSP chromosomes and store them in the corresponding property
Assert.AreEqual(((MultipleChromosome)chromosome).Chromosomes.Count, 3);
var tempMultiFitness = ((MultipleChromosome)chromosome).Chromosomes.Sum(c => fitness.Evaluate(c));
fitness = new MultipleFitness(fitness);
//Multi fitness should sum over the fitnesses of individual chromosomes
Assert.AreEqual(tempMultiFitness, fitness.Evaluate(chromosome));
population = new Population(30, 30, chromosome);
ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new GenerationNumberTermination(501)
};
ga.Start();
var bestTSPChromosome = (TspChromosome)((MultipleChromosome)ga.Population.BestChromosome).Chromosomes
.OrderByDescending(c => c.Fitness).First();
var multiChromosomesDistance = bestTSPChromosome.Distance;
Assert.Less(multiChromosomesDistance, simpleChromosomeDistance);
}
public void Run()
{
var selection = new InitialStrategySelection();
var crossover = new CutAndSpliceCrossover();
var mutation = new TworsMutation();
var fitness = new InitialStrategyFitness();
var chromosome = new InitialStrategyChromosome();
var population = new Population(50, 70, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new FitnessStagnationTermination(20);
Console.WriteLine("GA running...");
ga.Start();
Console.WriteLine("Best solution found has {0} fitness.", ga.BestChromosome.Fitness);
}
/// <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();
}
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();
}
private void btnStart_Click(object sender, EventArgs e)
{
Models.Instance i = new Models.Instance();
i.Days = 7;
i.Doctors = doctors;
int min = (int)numMin.Value;
int max = (int)numMax.Value;
var chromosome = new Models.Chromosome(21, i, r);
var population = new Population(min, max, chromosome);
var fitness = new Models.Fitness();
IMutation mutation = new TworsMutation();
ISelection selection = new RouletteWheelSelection();
ICrossover crossover = new OnePointCrossover(r.Next(20));
if (cbxMutation.SelectedItem.ToString() == "Insertion")
{
mutation = new InsertionMutation();
}
else if (cbxMutation.SelectedItem.ToString() == "Partial Shuffle")
{
mutation = new PartialShuffleMutation();
}
else if (cbxMutation.SelectedItem.ToString() == "Reverse Sequence")
{
mutation = new ReverseSequenceMutation();
}
if (cbxSelection.SelectedItem.ToString() == "Elitizam")
{
selection = new EliteSelection();
}
if (cbxCrossover.SelectedItem.ToString() == "Two-point")
{
int p1 = r.Next(19);
int p2 = r.Next(p1 + 1, 20);
crossover = new TwoPointCrossover(p1, p2);
}
else if (cbxCrossover.SelectedItem.ToString() == "Uniform")
{
crossover = new UniformCrossover();
}
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ITermination termination = new FitnessStagnationTermination(50);
if (cbxTermination.SelectedItem.ToString() == "Generation number")
{
termination = new GenerationNumberTermination(200);
}
ga.Termination = termination;
ga.MutationProbability = (float)numProbability.Value;
ga.Start();
Gene[] g = ga.BestChromosome.GetGenes();
dataView.Rows.Clear();
for (int j = 0; j < 7; j++)
{
string[] row = new string[] { ((List <int>)g[j * 3].Value)[0].ToString() + " " + ((List <int>)g[j * 3].Value)[1].ToString(),
((List <int>)g[j * 3 + 1].Value)[0].ToString() + " " + ((List <int>)g[j * 3 + 1].Value)[1].ToString(),
((List <int>)g[j * 3 + 2].Value)[0].ToString() + " " + ((List <int>)g[j * 3 + 2].Value)[1].ToString() };
dataView.Rows.Add(row);
dataView.Rows[j].HeaderCell.Value = (j + 1).ToString();
}
lblFitness.Text = ga.BestChromosome.Fitness.ToString() + " , generacija broj " + ga.GenerationsNumber.ToString();
}
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();
}
