private void Simulate()
{
CrossOverManager crossOver = new CrossOverManager(amount, amountOfWater, numberOfWashrooms, numberOfWatertanks, numberOfTents, numberOfPeople, tentCapacity, mapSizeX, mapSizeY, waterPerPersonToDrink);
int max = 0;
int min = 100;
minCamp = null;
maxCamp = null;
for (int i = 0; i < 50; i++)
{
Camp tmp = crossOver.Run(50);
int happy = tmp.GetHappiness();
if (max < happy)
{
max = happy;
maxCamp = tmp;
}
if (min > happy && happy != 0)
{
min = happy;
minCamp = tmp;
}
}
Debug.Log("Done! Best result is " + maxCamp.GetHappiness());
done = true;
}
static void Main(string[] args)
{
byte[] fileContet;
using (WebClient client = new WebClient())
{
fileContet = client.DownloadData("http://people.sc.fsu.edu/~jburkardt/datasets/tsp/p01_d.txt");
}
String input = Encoding.UTF8.GetString(fileContet).Trim();
int i = 0, j = 0;
string[] rows = input.Split('\n');
float [,] distancesVector = new float[rows.Length, rows.Length];
float maxPathLength = 0;
foreach (var row in rows)
{
j = 0;
foreach (var col in Regex.Split(row.Trim(), @"\s+"))
{
distancesVector[i, j] = float.Parse(col.Trim());
if (distancesVector[i, j] > maxPathLength)
{
maxPathLength = distancesVector[i, j];
}
j++;
}
i++;
}
int nodes = rows.Length;
//MutationManager.GetInstance().Register(new ClassicMutationResolver());
//MutationManager.GetInstance().Register(new SwapMutationResolver());
MutationManager.GetInstance().Register(new DividedMutation(nodes, new SwapMutationResolver(), new ClassicMutationResolver()));
CrossOverManager.GetInstance().Register(new DividedCrossOver(nodes, new PMXCrossOver(), new ClassicCrossOverResolver()));
CrossOverManager.GetInstance().Register(new DividedCrossOver(nodes, new CycleCrossOver(), new ClassicCrossOverResolver()));
var fitnessMatcherFabrik = new AutoevolutionaryFitnessMatcherDefaultImplementationFabrik();
var GAInfo = new AutoevolutionaryGeneticAlgorithmParameters <TravelSalesmanIndividual, AutoevolutionaryFitnessMatcherDefaultImplementation>
{
IndividualFabrik = new TravelSalesmanPathIndividualFabrik(nodes),
ScenarioGeneratior = new TravelSalesmanScenarioGenerator(distancesVector, nodes, maxPathLength),
EvolutionaryInformationFabrik = new AutoEvolutionaryInformationFabrik <AutoevolutionaryFitnessMatcherDefaultImplementation>(fitnessMatcherFabrik),
MaxPopulation = 100,
InitialLives = 3,
AprovedPorcentage = 0.5f,
ApariateTournamentCount = 5,
DeathTournamentCount = 6,
ForeingersByGeneration = 10
};
var GA = new AutoEvolutionaryGeneticAlgorithm <TravelSalesmanIndividual, AutoevolutionaryFitnessMatcherDefaultImplementation>(GAInfo);
//Console.Out.WriteLine(Arrays.PrettyPrintMatrix<float>(scenarioGenerator.DistancesVector));
for (int g = 0; g < 5000; g++)
{
GA.AdvanceGenerations(1);
var bestIndividual = GA.Population.MaxBy(o => o.getNormalizedFitness());
Console.Out.WriteLine("Generation: " + GA.GenerationNumber);
Console.Out.WriteLine("AVG apariate dominance porcentage: " + GA.Population.Average(o => o.EvolutionInformtaion.CrossOverGenDominancePorcentage));
Console.Out.WriteLine("Best apariate dominance porcentage: " + bestIndividual.EvolutionInformtaion.CrossOverGenDominancePorcentage);
Console.Out.WriteLine("AVG mutate amplitude: " + GA.Population.Average(o => o.EvolutionInformtaion.MutateGenAmplitudePorcentage));
Console.Out.WriteLine("Best mutate amplitude: " + bestIndividual.EvolutionInformtaion.MutateGenAmplitudePorcentage);
Console.Out.WriteLine("AVG mutate choose prob: " + GA.Population.Average(o => o.EvolutionInformtaion.MutateGenChooseProbability));
Console.Out.WriteLine("Best mutate choose prob: " + bestIndividual.EvolutionInformtaion.MutateGenChooseProbability);
Console.Out.WriteLine("Best mutation type prob: " + bestIndividual.EvolutionInformtaion.MutationTypeProbability[0]);
Console.Out.WriteLine("Best crossover type prob: " + bestIndividual.EvolutionInformtaion.CrossOverTypeProbability[0] + ", " + bestIndividual.EvolutionInformtaion.CrossOverTypeProbability[1]);
Console.Out.WriteLine("Actual Population: " + GA.Population.Count);
Console.Out.WriteLine("Best path: " + String.Join(",", bestIndividual.Individual.travelOrder));
Console.Out.WriteLine("Best fitness: " + bestIndividual.getNormalizedFitness());
TravelSalesmanFitness fitness = (TravelSalesmanFitness)(bestIndividual.Fitness);
Console.Out.WriteLine("Best fitness: " + fitness.TotalDistance);
}
while (true)
{
Thread.Sleep(1000);
}
}
public Genome Cross(int type, Genome other, float dominance_porcentage)
{
return(new Genome(Descriptors, CrossOverManager.GetInstance().Cross(type, this.Gens, other.Gens, dominance_porcentage)));
}
public AutoevolutionaryInformation Create(List <Gen> gens)
{
return(new AutoevolutionaryInformation(
FitnessMatcherFabrik.Create(gens.Skip(AutoEvolutionaryInformationGeneticDescriptor.Count + MutationManager.GetInstance().GetMutationTypes() + CrossOverManager.GetInstance().GetCrossOverTypes()).ToList()),
gens[0].Value,
gens[1].Value,
gens[2].Value,
gens.Skip(3).Take(MutationManager.GetInstance().GetMutationTypes()).Select(o => o.Value).ToList(),
gens.Skip(3 + MutationManager.GetInstance().GetMutationTypes()).Take(CrossOverManager.GetInstance().GetCrossOverTypes()).Select(o => o.Value).ToList()));
}
public int GetGenCount()
{
return(AutoEvolutionaryInformationGeneticDescriptor.Count + MutationManager.GetInstance().GetMutationTypes() + CrossOverManager.GetInstance().GetCrossOverTypes() + FitnessMatcherFabrik.GetGenCount());
}
public List <GenDescriptor> GetGeneticDescriptor()
{
var descriptor = AutoEvolutionaryInformationGeneticDescriptor.ToList();
descriptor.AddRange(Enumerable.Repeat(new GenDescriptor("Mutation type prob", 0, 1, 0.001f), MutationManager.GetInstance().GetMutationTypes()));
descriptor.AddRange(Enumerable.Repeat(new GenDescriptor("Apariation type prob", 0, 1, 0.001f), CrossOverManager.GetInstance().GetCrossOverTypes()));
descriptor.AddRange(FitnessMatcherFabrik.GetGeneticDescriptor());
return(descriptor);
}
