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";
}
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();
}