/**
* Evolves the specified variables using the SSX operator.
*
* @param s1 the first subset
* @param s2 the second subset
*/
public static void evolve(Subset s1, Subset s2) {
int k = s1.getK();
Set<Integer> p1set = s1.getSet();
Set<Integer> p2set = s2.getSet();
for (int i = 0; i < k; i++) {
if (!p1set.contains(s2.get(i)) && !p2set.contains(s1.get(i)) && PRNG.nextBoolean()) {
int temp = s1.get(i);
s1.set(i, s2.get(i));
s2.set(i, temp);
}
}
}
@Override
public Solution generate() {
Solution solution = newSolution();
for (int i = 0; i < numberOfObjectives - 1; i++) {
((RealVariable) solution.getVariable(i)).setValue(PRNG.nextDouble());
}
for (int i = numberOfObjectives - 1; i < numberOfVariables; i++) {
((RealVariable) solution.getVariable(i)).setValue(0);
}
evaluate(solution);
return solution;
}
@Override
protected void iterate() {
ReferenceVectorGuidedPopulation population = getPopulation();
Population offspring = new Population();
int populationSize = population.size();
// update the scaling factor for computing the angle-penalized distance
population.setScalingFactor(Math.min(generation / (double) maxGeneration, 1.0));
// create a random permutation of the population indices
List<Integer> indices = new ArrayList<Integer>();
for (int i = 0; i < populationSize; i++) {
indices.add(i);
}
PRNG.shuffle(indices);
// add an extra entry so the number of indices is even
if (indices.size() % 2 == 1) {
indices.add(indices.get(0));
}
// generate the offspring
for (int i = 0; i < indices.size(); i += 2) {
Solution[] parents =
new Solution[] {population.get(indices.get(i)), population.get(indices.get(i + 1))};
Solution[] children = variation.evolve(parents);
offspring.addAll(children);
}
evaluateAll(offspring);
// select the survivors
population.addAll(offspring);
population.truncate();
// periodically normalize the reference vectors
if ((generation > 0) && (generation % adaptFrequency == 0)) {
population.adapt();
}
generation++;
}
@Override
public Solution[] evolve(Solution[] parents) {
Solution result1 = parents[0].copy();
Solution result2 = parents[1].copy();
for (int i = 0; i < result1.getNumberOfVariables(); i++) {
Variable variable1 = result1.getVariable(i);
Variable variable2 = result2.getVariable(i);
if ((PRNG.nextDouble() <= probability)
&& (variable1 instanceof Subset)
&& (variable2 instanceof Subset)) {
evolve((Subset) variable1, (Subset) variable2);
}
}
return new Solution[] {result1, result2};
}
