/** It applies the migration stage */
void Migration() {
int i, j, num;
double u;
/* First, the individual in the population are ordered according to their fitness */
Collections.sort(Poblacion);
/* The second half (the worst one) is radomly initialized again */
for (j = (tamPoblacion / 2); j < tamPoblacion; j++) {
/* First, the antecedent */
for (i = 0; i < entradas; i++) {
Poblacion.get(j).antecedente[i].m =
Randomize.RanddoubleClosed(train.getMin(i), train.getMax(i));
Poblacion.get(j).antecedente[i].sigma = Randomize.RandintClosed(1, 4);
}
/* Secondly, the consequent */
do {
num = 0;
for (i = 0; i < entradas; i++) {
u = Randomize.RandClosed();
/* The term is used in the consequent */
if (u < 0.5) {
Poblacion.get(j).consecuente[i] = Randomize.RanddoubleClosed(-1.0, 1.0);
// Poblacion.get(j).consecuente[entradas] =
// Randomize.RanddoubleClosed(-45.0, 45.0);
if (Poblacion.get(j).consecuente[i] != 0.0) {
num++;
}
}
/* The term is NOT used in the consequent */
else {
Poblacion.get(j).consecuente[i] = 0.0;
}
}
u = Randomize.RandClosed();
/* The term is used in the consequent */
if (u < 0.5) {
Poblacion.get(j).consecuente[entradas] =
Randomize.RanddoubleClosed(
-1.0 * ((train.getMax(entradas) - train.getMin(entradas)) / 2.0),
((train.getMax(entradas) - train.getMin(entradas)) / 2.0));
// Poblacion.get(j).consecuente[entradas] =
// Randomize.RanddoubleClosed(-45.0, 45.0);
// Poblacion.get(j).consecuente[entradas] =
// Randomize.RanddoubleClosed(train.getMin(entradas), train.getMax(entradas));
if (Poblacion.get(j).consecuente[entradas] != 0.0) {
num++;
}
}
/* The term is NOT used in the consequent */
else {
Poblacion.get(j).consecuente[entradas] = 0.0;
}
} while (num == 0);
}
}
/** It applies the reproduction stage */
void Reproduction() {
int i, madre, padre;
/* First, the individual in the population are ordered according to their fitness */
Collections.sort(Poblacion);
/* Create the new population */
Poblacion2.clear();
Hijos.clear();
/* Top-half best-performing individuals will advance to the next generation */
for (i = 0; i < (tamPoblacion / 2); i++) {
Individual indi = new Individual(Poblacion.get(i));
Poblacion2.add(indi);
}
/* The remaining half is generated by performing crossover operations on individuals
in the top half */
while (Poblacion.size() != (Poblacion2.size() + Hijos.size())) {
/* 2 parents are selected */
madre = Selection();
do {
padre = Selection();
} while (madre == padre);
/* 2 children are created by crossover operator */
Crossover(madre, padre);
}
/* Create the population for the next generation */
Poblacion.clear();
for (i = 0; i < Poblacion2.size(); i++) {
Individual indi = new Individual(Poblacion2.get(i));
Poblacion.add(indi);
}
for (i = 0; i < Hijos.size(); i++) {
Individual indi = new Individual(Hijos.get(i));
Poblacion.add(indi);
}
}
/** Function to sort the rule base */
public void sort() {
Collections.sort(this.ruleBase);
}
