/** Applies a roulette wheel selection */
public void selection() {
RuleSet temp[];
double probability[] = new double[long_poblacion];
double total;
double prob;
int sel;
temp = new RuleSet[long_poblacion];
// sort the poblation in order of fitness
Arrays.sort(poblacion, Collections.reverseOrder());
probability[0] = poblacion[0].getFitness();
for (int i = 1; i < long_poblacion; i++) {
probability[i] = probability[i - 1] + poblacion[i].getFitness();
}
total = probability[long_poblacion - 1];
for (int i = 0; i < long_poblacion; i++) {
probability[i] /= total;
}
for (int i = 0; i < long_poblacion; i++) {
prob = Randomize.Rand();
sel = -1;
for (int j = 0; j < long_poblacion && sel == -1; j++) {
if (probability[j] > prob) sel = j;
}
temp[i] = new RuleSet(poblacion[sel]);
}
previousPob = poblacion;
poblacion = temp;
}
/**
* It runs the Single-attribute Evolution Module (SEM) algorithm to obtain a rule set of ONE
* attribute
*/
public void run() {
boolean endCondition = false;
int gen = 0;
int stagnation = 0;
evaluate();
while (!endCondition) {
tournament_selection();
crossOver();
mutate();
elitism();
evaluate();
Arrays.sort(poblacion, Collections.reverseOrder());
gen++;
if (bestCR != poblacion[0].getFitness()) stagnation = 0;
else stagnation++;
if (gen > generationLimit || stagnation > stagnationLimit || poblacion[0].getFitness() == 1.0)
endCondition = true;
bestCR = poblacion[0].getFitness();
}
}