/** 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);
}
}
public void removeRules() {
int i, pos;
double minRate, rate;
Rule rule;
minRate = 1.0;
pos = -1;
for (i = 0; i < this.ruleBase.size(); i++) {
rule = this.ruleBase.get(i);
if (rule.getRightN() < 1) {
this.ruleBase.remove(i);
i--;
} else {
rate = (1.0 * rule.getRightN()) / (1.0 * rule.getWrongN() + rule.getRightN());
if (rate < minRate) {
minRate = rate;
pos = i;
}
}
}
if (ruleBase.size() > 0 && pos > -1) this.ruleBase.remove(pos);
}