public individuo function_mutate(individuo ind, individuo[] Poblation, IMG picture, int umbral, int umbral_d)
{//mejorar
individuo copy = ind;
int pivot = 0, test = 0;
bool op = true;
while (op)//Mutar en X
{
pivot = ale.Next(10);
copy.Xbin[pivot] = (copy.Xbin[pivot] == 0) ? 1 : 0;
test = BinToDeci(copy.Xbin);
op = (test < picture.getW()) ? false : true;
}
op = true;
while (op)//Mutar en Y
{
pivot = ale.Next(10);
copy.Ybin[pivot] = (copy.Ybin[pivot] == 0) ? 1 : 0;
test = BinToDeci(copy.Ybin);
op = (test < picture.getH()) ? false : true;
}
copy.X = BinToDeci(copy.Xbin);
copy.Y = BinToDeci(copy.Ybin);
copy.Value = picture.gray2(copy, umbral);
copy.Xdist = Xdistant(Poblation, copy, umbral_d);
copy.Phenotype = copy.Value + copy.Xdist;
return((copy.Phenotype >= ind.Phenotype) ? copy : ind);
}
private void IniciarAE_Click(object sender, EventArgs e)
{
CleanOut.Enabled = true;
arraypoblation = pb.CrearPoblacion(picture.getW(), picture.getH(), int.Parse(numInd.Text), int.Parse(Umbral.Text), picture);
Poblacion ctlPoblation = new Poblacion(); //
ctlPoblation.Generation = 0;
Salida.Items.Add("Población inicial generada");
Salida.Refresh();
PrintPoblation(arraypoblation);
Salida.Items.Add("Graficación de la población inicial");
Salida.Refresh();//promedio o calidad poblacional
ctlPoblation.Derivative[ctlPoblation.Generation] = pb.Average(arraypoblation);
Salida.Items.Add("Calidad población inicial: " + ctlPoblation.Derivative[ctlPoblation.Generation].ToString());
Salida.Refresh();
int derivada = ctlPoblation.derivate_calculation(ctlPoblation.Derivative, ctlPoblation.Derivative[ctlPoblation.Generation]), cont = 0;
Salida.Items.Add("Derivada : " + derivada);
while (derivada == 0)
{
arraypoblation = pb.Create_Generation(arraypoblation,
(float)Convert.ToSingle(Cruce.Text.Replace('.', ',')),
int.Parse(Umbral_d.Text),
picture,
int.Parse(Umbral.Text));
arraypoblation = pb.UpdateState(arraypoblation);
ctlPoblation.Generation++;
cont++;
Salida.Items.Add("Generación: " + cont);
Salida.Refresh();
if (ctlPoblation.Generation == 10)
{
ctlPoblation.Generation = 0;
}
ctlPoblation.Derivative[ctlPoblation.Generation] = pb.Average(arraypoblation);
Salida.Items.Add("Calidad de la población: " + ctlPoblation.Derivative[ctlPoblation.Generation].ToString());
Salida.Refresh();
derivada = ctlPoblation.derivate_calculation(ctlPoblation.Derivative, ctlPoblation.Derivative[ctlPoblation.Generation]);
}
Salida.Items.Add("Derivada : " + derivada);
Salida.Items.Add("Proceso terminado");
Salida.Refresh();
//MostrarPoblacion(arraypoblation);
}
public individuo[] function_crossing(int ind1, int ind2, individuo[] poblation, int umbral_d, IMG picture, int umbral)
{
int pivot = 0, test1 = 0, test2 = 0, max = 0;
int[] indX = new int[10], ind2X = new int[10], indY = new int[10], ind2Y = new int[10], temp = new int[10], Phenotypes = new int[4];
bool option = true;
while (option) //violada en x
{
pivot = ale.Next(10);
indX = poblation[ind1].Xbin;
ind2X = poblation[ind2].Xbin;
temp = indX;
indX[pivot] = ind2X[pivot];
ind2X[pivot] = temp[pivot];
test1 = BinToDeci(indX);
test2 = BinToDeci(ind2X);
option = (test1 < picture.getW()) && (test2 < picture.getW()) ? false : true;
}
option = true;
while (option) //violada en y
{
pivot = ale.Next(10);
indY = poblation[ind1].Ybin;
ind2Y = poblation[ind2].Ybin;
temp = indY;
indY[pivot] = ind2Y[pivot];
ind2Y[pivot] = temp[pivot];
test1 = BinToDeci(indY);
test2 = BinToDeci(ind2Y);
option = (test1 < picture.getH()) && (test2 < picture.getH()) ? false : true;
} //Determinacion del fenotipo de los violados
individuo son1 = new individuo(), son2 = new individuo();
son1.Xbin = indX;
son1.Ybin = indY;
son1.X = BinToDeci(son1.Xbin);
son1.Y = BinToDeci(son1.Ybin);
son1.Xdist = Xdistant(poblation, son1, umbral_d);
son1.Value = picture.gray2(son1, umbral);
son1.Phenotype = son1.Xdist + son1.Value;
son2.Xbin = ind2X;
son2.Ybin = ind2Y;
son2.X = BinToDeci(son2.Xbin);
son2.Y = BinToDeci(son2.Ybin);
son2.Xdist = Xdistant(poblation, son2, umbral_d);
son2.Value = picture.gray2(son2, umbral);
son2.Phenotype = son2.Xdist + son2.Value;
List <individuo> poblationFinal = new List <individuo>();
Phenotypes[0] = poblation[ind1].Phenotype;
Phenotypes[1] = poblation[ind2].Phenotype;
Phenotypes[2] = son1.Phenotype;
Phenotypes[3] = son2.Phenotype;
for (int i = 0; i < Phenotypes.Length / 2; i++)
{
max = getBest(Phenotypes);
switch (max)
{
case 0:
poblationFinal.Add(poblation[ind1]);
break;
case 1:
poblationFinal.Add(poblation[ind2]);
break;
case 2:
poblationFinal.Add(son1);
break;
case 3:
poblationFinal.Add(son2);
break;
}
Phenotypes[max] = 0;
}
return(poblationFinal.ToArray()); //convertir list to array*/
}
