internal void informarResultados(GaEventArgs e, long cantIteracionesMax)
{
Chromosome cromo;
informarNuevaLinea(Color.Indigo, string.Format("Cantidad de individuos: {0}, Cantidad de iteraciones Máxima: {1}", e.Population.PopulationSize, cantIteracionesMax));
informarNuevaLinea(Color.Brown, string.Format("El mayor valor de ajuste alcanzado por la población final: {0}", e.Population.MaximumFitness));
informarNuevaLinea(Color.Red, string.Format("Número de iteración: {0}, Cantidad de cromosomas evaluados:", e.Generation, e.Evaluations));
informarNuevaLinea(Color.BlueViolet, "Mejor solución de la población final:");
cromo = e.Population.Solutions.Find(x => x.Fitness == e.Population.MaximumFitness);
informarCromosoma(cromo);
informarNuevaLinea(Color.BlueViolet, "Mejor solución de la corrida:");
if (mejorIteracion != 0)
{
informarNuevaLinea(Color.Red, string.Format("Número de iteración: {0}, Cantidad de cromosomas evaluados: {1}", mejorIteracion, mejorGeneracion));
informarCromosoma(mejorSolucion);
}
else informarNuevaLinea(Color.HotPink, "No hubo una mejor solucion durante la corrida");
informarNuevaLinea(Color.BlueViolet, "Población de la solución:");
foreach (Chromosome cromosoma in e.Population.Solutions)
{
informarCromosoma(cromosoma);
}
}
public void ShowChart(GaEventArgs e)
{
chart.Show();
var maxIndividualChromosome = e.Population.GetTop(1).First();
var maxIndividual = Acertijo.Instance.CrearModelos(maxIndividualChromosome);
resultsText.Text = "Generaciones: " + e.Generation + "\n" +
"Población final: " + "\n" +
"Tamaño: " + e.Population.PopulationSize + " individuos" + "\n" +
"Mejor individuo: " + "\n"+
"Mejor individuo Fitness: " + e.Population.MaximumFitness.ToString() + "\n";
foreach (var persona in maxIndividual)
{
resultsText.Text += "Nombre: " + persona.Name + ", Nacionalidad: " + persona.Nationality + ", Vehículo: " + persona.Vehicle + ", Gesto: " + persona.MouthAction + "\n";
}
}
private void Ga_OnGenerationComplete(object sender, GaEventArgs e)
{
var fittest = e.Population.GetTop(1)[0];
for (int i = 0; i < fittest.Genes.Count; i++)
{
StringBuilder sb = new StringBuilder();
int stock = stocks[i];
int[] solitems = (int[])fittest.Genes[i].ObjectValue;
int sum = solitems.Sum();
Debug.WriteLine(string.Format("Stock {0} - Length {1} with {2}", i, stock, string.Join(",", solitems)));
}
}
private void OnGenerationComplete(GaEventArgs e)
{
progressBar.Value = (int) (Decimal.Divide(e.Generation, AGHelper.cantidadDeIteraciones) * 100);
currentResultsTab.AddPoint(e.Generation, e.Population.MaximumFitness);
currentResultsTab.ShowChart(e);
}
private void Ga_OnGenerationComplete(object sender, GaEventArgs e)
{
}
private void Ga_OnRunComplete(object sender, GaEventArgs e)
{
var fittest = e.Population.GetTop(1)[0];
Debug.WriteLine("Fittest chromosome or best solution");
DebugChromosome(fittest);
int totCut=0,totUncut=0, totWaste = 0, totStock = 0,totToCut=0;
foreach (var gene in fittest.Genes)
{
var cutplan = (gene.ObjectValue as CutPlan);
totWaste += cutplan.Waste==cutplan.StockLength?0:cutplan.Waste;
totStock += cutplan.Waste == cutplan.StockLength ? 0 : cutplan.StockLength;
totCut += cutplan.CutLength;
totUncut += cutplan.Waste == cutplan.StockLength ? cutplan.StockLength : 0;
}
items.ForEach(item => totToCut += item.TotalLength);
Debug.WriteLine("Total To Cut:" + totToCut);
Debug.WriteLine("Total Cut:" + totCut);
Debug.WriteLine("Total Waste:" + totWaste);
Debug.WriteLine("Total Stock:" + totStock);
Debug.WriteLine(string.Format("Percentage Waste {0:P2} ", (totWaste*1.0 / totStock)));
Debug.WriteLine("Uncut Stock:" + totUncut);
if (totToCut > totCut)
{
Debug.WriteLine("No stock to cut all need");
Debug.WriteLine(totToCut - totCut + " rest tot cut");
}
/* for (int i = 0; i < fittest.Genes.Count; i++)
{
StringBuilder sb = new StringBuilder();
int stock = stocks[i];
int[] solitems = (int[])fittest.Genes[i].ObjectValue;
int sum = solitems.Sum();
Debug.WriteLine(string.Format("Stock {0} - Length {1} with {2}", i, stock, string.Join(",", solitems)));
}*/
}
static void ga_OnRunComplete(object sender, GaEventArgs e)
{
var fittest = e.Population.GetTop(1)[0];
foreach (var gene in fittest.Genes)
{
Log.Trace(System.Convert.ToString((int) gene.RealValue));
}
}
private static void ga_OnGenerationComplete(object sender, GaEventArgs e)
{
var fittest = e.Population.GetTop(1)[0];
var sharpe = RunAlgorithm(fittest);
Log.Trace("Generation: {0}, Fitness: {1},Distance: {2}", e.Generation, fittest.Fitness, sharpe);
}
// Callback for when a generation completes its iteration.
private static void ga_OnGenerationComplete(object sender, GaEventArgs e)
{
// We print in the console the current results of the algorithm for every 100 generations
// when not in calibration mode. When calibration, we print only for the last generation.
if ((!IS_CALIBRATION && e.Generation % 100 == 0) || e.Generation == NUMBER_OF_GENERATIONS)
{
var fittest = getFittestChromosome(e.Population);
var distanceToTravel = 0.0;
if (!FITNESS_CONSIDERS_PARTITIONS)
{
distanceToTravel = CalculateDistance(fittest);
}
else
{
var greedy_mTSP =
new GreedyAlgorithm(Utils.fittestToCitiesList(CITIES, fittest), DEPOT, NUMBER_OF_DRONES);
distanceToTravel = greedy_mTSP.solve().Item1;
}
Console.WriteLine("Generation: {0}, Fitness: {1}, Distance: {2}", e.Generation, fittest.Fitness, distanceToTravel);
}
}
private void ga_OnGenerationComplete(object sender, GaEventArgs e)
{
var fittest = e.Population.GetTop(1)[0];
asignarMejorPoblacion(e);
if (OnGenerationCompleteCallback != null)
{
OnGenerationCompleteCallback(e);
}
}
private void asignarMejorPoblacion(GaEventArgs e)
{
Chromosome cromo = e.Population.Solutions.Find(x => x.Fitness == e.Population.MaximumFitness);
if (cromo.Fitness > mejorFitness)
{
mejorFitness = cromo.Fitness;
logger.asignarMejorSolucion(cromo, e.Generation, e.Evaluations);
}
}
private void ga_OnGenerationComplete(object sender, GaEventArgs e)
{
var chromosome = e.Population.GetTop(1)[0];
List<int> tmp = GetUsableValues(chromosome.ToBinaryString());
string output = String.Format("inhibitorKills {0}\t towerKills {1}\t firstTower {2}\t firstBlood {3}\t firstBaron {4}\t firstInhibitor {5}\t firstDragon {6}\t baronKills {7}\t dragonKills {8}",
tmp[0] / 250.0, tmp[1] / 250.0, tmp[2], tmp[3], tmp[4], tmp[5], tmp[6], tmp[7] / 250.0, tmp[8] / 250.0);
Console.WriteLine(output + Environment.NewLine + chromosome.Fitness + Environment.NewLine);
}
static void ga_OnRunComplete(object sender, GaEventArgs e)
{
var fittest = e.Population.GetTop(1)[0];
foreach (var gene in fittest.Genes)
{
Console.WriteLine((int)gene.RealValue);
}
}
static void ga_OnRunComplete(object sender, GaEventArgs e)
{
var fittest = e.Population.GetTop(1)[0];
foreach (var gene in fittest.Genes)
{
ConfigVars v = (ConfigVars)gene.ObjectValue;
foreach(KeyValuePair<string,object> kvp in v.vars)
Console.WriteLine("Variable {0}:, value {1}",kvp.Key,kvp.Value.ToString());
}
}
static void ga_OnRunComplete(object sender, GaEventArgs e)
{
var fittest = getFittestChromosome(e.Population);
// Call the algorithm to generate the mTSP solution.
var greedy_mTSP = new GreedyAlgorithm(Utils.fittestToCitiesList(CITIES, fittest), DEPOT, NUMBER_OF_DRONES);
solution = greedy_mTSP.solve().Item1;
partitionPoints = greedy_mTSP.solve().Item2;
timeElapsed = STOPWATCH.ElapsedMilliseconds;
fittestChromosome = fittest;
Console.WriteLine("Final solution (MinMax cost): {0}", solution);
Console.WriteLine("Time elapsed: {0} ms", STOPWATCH.ElapsedMilliseconds);
}
private void OnRunComplete(GaEventArgs e)
{
currentResultsTab.ShowChart(e);
progressBar.Value = 0;
groupBox1.Enabled = true;
Iniciar.Enabled = true;
}
private void Ga_OnRunComplete(object sender, GaEventArgs e)
{
#if DEBUG
Debug.WriteLine("Run Complete ...");
#endif
var args = new SolverEventArgs(e.Population, e.Generation, e.Evaluations, Beams);
OnSolved(this, args);
}
void ga_OnRunComplete(object sender, GaEventArgs e)
{
reloj.Stop();
if (OnRunCompleteCallback != null)
{
OnRunCompleteCallback(e);
}
}
