@Override
protected Individual select(EvolutionaryAlgorithm algorithm, List<Individual> individuals) {
double random;
double acum;
int n = individuals.size();
double min = 2 - this.max;
double range = this.max - min;
Individual individual;
int i;
// Se ordena la lista de individuos según el comparador en orden inverso:
Collections.sort(individuals, algorithm.getComparator());
Collections.reverse(individuals);
// Ahora la posicion en la lista de cada individuo indica su rank:
random = EAFRandom.nextDouble();
acum = 0.0;
i = 0;
do {
i++;
acum += (1.0 / n) * (this.max - range * ((double) (i - 1.0) / (double) (n - 1.0)));
} while (acum < random);
individual = individuals.get(--i);
return (Individual) individual.clone();
}
@Override
public List<Individual> operate(EvolutionaryAlgorithm algorithm, List<Individual> individuals)
throws OperatorException {
MacroevolutionaryAlgorithm alg = (MacroevolutionaryAlgorithm) algorithm;
FitnessComparator<Individual> comparator = algorithm.getComparator();
Wxy wxy;
MaIndividual indA, indB;
double weight;
int survivors = 0;
if (comparator instanceof MaximizingFitnessComparator) {
wxy = new WxyMaximizing();
} else {
if (comparator instanceof MinimizingFitnessComparator) {
wxy = new WxyMinimizing();
} else {
throw new OperatorException("Wrong comparator for Inherit Extintion operator");
}
}
for (int i = 0; i < individuals.size(); i++) {
indA = (MaIndividual) individuals.get(i);
if (indA.getBestFitness() != indA.getFitness()) {
indA.setSurvivor(false);
} else {
for (int j = i + 1; j < individuals.size(); j++) {
indB = (MaIndividual) individuals.get(j);
if (!indA.isSurvivor() || !indB.isSurvivor()) {
alg.setWxy(i, j, wxy.get(indA, indB));
}
}
weight = 0;
for (int j = 0; j < i; j++) {
weight -= alg.getWxy(j, i);
}
for (int j = i + 1; j < individuals.size(); j++) {
weight += alg.getWxy(i, j);
}
if (weight > 0) {
indA.setSurvivor(true);
survivors++;
} else {
if (weight < 0) {
indA.setSurvivor(false);
} else {
if (EAFRandom.nextDouble() < 0.5) {
indA.setSurvivor(true);
survivors++;
} else {
indA.setSurvivor(false);
}
}
}
}
}
// We always need at least one survivor
if (survivors == 0) {
BestIndividual chooser = new BestIndividual();
((MaIndividual) chooser.get(algorithm, individuals, null)).setSurvivor(true);
}
return individuals;
}
