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 Examinar_Click(object sender, EventArgs e)
{
try
{
if (imagendecarga.ShowDialog() == DialogResult.OK)
{
string img = imagendecarga.FileName;
ImagenInicial.Image = Image.FromFile(img);
picture = new IMG(ImagenInicial.Image.Width, ImagenInicial.Image.Height, ImagenInicial.Image);
Salida.Items.Add("Imagen cargada ");
IniciarAE.Enabled = true;
}
}
catch (Exception ex)
{
MessageBox.Show("El archivo seleccionado no es un tipo de imagen válido" + ex.ToString());
}
}
public individuo[] CrearPoblacion(int W, int H, int Size, int umbral, IMG picture)//Crea la poblacion inicial
{
ind = new individuo[Size];
for (int n = 0; n < Size; n++)
{
ind[n] = new individuo();
ind[n].X = ale.Next(0, W);
ind[n].Y = ale.Next(0, H);
ind[n].Evolved = false;
ind[n].Xbin = DeciToBin(ind[n].X);
ind[n].Ybin = DeciToBin(ind[n].Y);
ind[n].Value = picture.gray2(ind[n], umbral);
}
for (int n = 0; n < Size; n++)
{
ind[n].Xdist = Xdistant(ind, ind[n], umbral);
ind[n].Phenotype = ind[n].Xdist + ind[n].Value;
}
return(ind);
}
public individuo[] Create_Generation(individuo[] poblation, float cruce, int umbral_d, IMG picture, int umbral)
{
int num_cruces = (int)(cruce * (float)poblation.Length), ind1 = 0, ind2 = 0, num_mutations = 0;
num_cruces += ((num_cruces % 2 == 1)) ? 1 : 0;
num_cruces /= 2;
num_mutations = (poblation.Length - (num_cruces * 2));
bool op = true;
for (int i = 0; i < num_cruces; i++)
{
while (op)
{
ind1 = ale.Next(poblation.Length);
ind2 = ale.Next(poblation.Length);
op = (poblation[ind1].Evolved == false) && (poblation[ind2].Evolved == false) ? false : true;
}
individuo[] cruzados = function_crossing(ind1, ind2, poblation.ToArray(), umbral_d, picture, umbral);
poblation[ind1] = cruzados[0];
poblation[ind1].Evolved = true;
poblation[ind2] = cruzados[1];
poblation[ind2].Evolved = true;
}
for (int i = 0; i < num_mutations; i++)
{
ind1 = 0;
op = true;
while (op)
{
ind1 = ale.Next(poblation.Length);
op = (poblation[ind1].Evolved == false) ? false : true;
}
poblation[ind1] = function_mutate(poblation[ind1], poblation.ToArray(), picture, umbral, umbral_d);
poblation[ind1].Evolved = true;
}
return(poblation);
}
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*/
}
