@Override
public <E extends Entity> List<E> crossover(List<E> parentCollection) {
Preconditions.checkArgument(
parentCollection.size() == 2, "BlendCrossoverStrategy requires 2 parents.");
// How do we handle variable sizes? Resizing the entities?
E offspring1 = (E) parentCollection.get(0).getClone();
E offspring2 = (E) parentCollection.get(1).getClone();
Vector parentChromosome1 = (Vector) parentCollection.get(0).getCandidateSolution();
Vector parentChromosome2 = (Vector) parentCollection.get(1).getCandidateSolution();
Vector.Builder offspringChromosome1 = Vector.newBuilder();
Vector.Builder offspringChromosome2 = Vector.newBuilder();
int sizeParent1 = parentChromosome1.size();
int sizeParent2 = parentChromosome2.size();
int minDimension = Math.min(sizeParent1, sizeParent2);
for (int i = 0; i < minDimension; i++) {
double gamma =
(1 + 2 * alpha.getParameter()) * random.getRandomNumber() - alpha.getParameter();
double value1 =
(1 - gamma) * parentChromosome1.doubleValueOf(i)
+ gamma * parentChromosome2.doubleValueOf(i);
double value2 =
(1 - gamma) * parentChromosome2.doubleValueOf(i)
+ gamma * parentChromosome1.doubleValueOf(i);
offspringChromosome1.add(Real.valueOf(value1, parentChromosome1.boundsOf(i)));
offspringChromosome2.add(Real.valueOf(value2, parentChromosome1.boundsOf(i)));
}
offspring1.setCandidateSolution(offspringChromosome1.build());
offspring2.setCandidateSolution(offspringChromosome2.build());
return Arrays.asList(offspring1, offspring2);
}
/** {@inheritDoc} */
@Override
public <E extends Entity> List<E> crossover(List<E> parentCollection) {
checkState(
parentCollection.size() >= 3,
"There must be a minimum of three parents to perform UNDX crossover.");
checkState(
numberOfOffspring > 0,
"At least one offspring must be generated. Check 'numberOfOffspring'.");
List<Vector> solutions = Entities.<Vector>getCandidateSolutions(parentCollection);
List<E> offspring = Lists.newArrayListWithCapacity(numberOfOffspring);
UniformDistribution randomParent = new UniformDistribution();
final int k = solutions.size();
final int n = solutions.get(0).size();
for (int os = 0; os < numberOfOffspring; os++) {
// get index of main parent and put its solution at the end of the list
int parent = (int) randomParent.getRandomNumber(0.0, k);
Collections.swap(solutions, parent, k - 1);
List<Vector> e_zeta = new ArrayList<Vector>();
// calculate mean of parents except main parent
Vector g = Vectors.mean(solutions.subList(0, k - 1));
// basis vectors defined by parents
for (int i = 0; i < k - 1; i++) {
Vector d = solutions.get(i).subtract(g);
if (!d.isZero()) {
double dbar = d.length();
Vector e = d.orthogonalize(e_zeta);
if (!e.isZero()) {
e_zeta.add(e.normalize().multiply(dbar));
}
}
}
final double D = solutions.get(k - 1).subtract(g).length();
// create the remaining basis vectors
List<Vector> e_eta = Lists.newArrayList();
e_eta.add(solutions.get(k - 1).subtract(g));
for (int i = 0; i < n - e_zeta.size() - 1; i++) {
Vector d = Vector.newBuilder().copyOf(g).buildRandom();
e_eta.add(d);
}
e_eta = Vectors.orthonormalize(e_eta);
// construct the offspring
Vector variables = Vector.copyOf(g);
if (!useIndividualProviders) {
for (int i = 0; i < e_zeta.size(); i++) {
variables =
variables.plus(
e_zeta.get(i).multiply(random.getRandomNumber(0.0, sigma1.getParameter())));
}
for (int i = 0; i < e_eta.size(); i++) {
variables =
variables.plus(
e_eta
.get(i)
.multiply(
D * random.getRandomNumber(0.0, sigma2.getParameter() / Math.sqrt(n))));
}
} else {
for (int i = 0; i < e_zeta.size(); i++) {
variables =
variables.plus(
e_zeta
.get(i)
.multiply(
new Supplier<Number>() {
@Override
public Number get() {
return random.getRandomNumber(0.0, sigma1.getParameter());
}
}));
}
for (int i = 0; i < e_eta.size(); i++) {
variables =
variables.plus(
e_eta
.get(i)
.multiply(
new Supplier<Number>() {
@Override
public Number get() {
return D
* random.getRandomNumber(
0.0, sigma2.getParameter() / Math.sqrt(n));
}
}));
}
}
E child = (E) parentCollection.get(parent).getClone();
child.setCandidateSolution(variables);
offspring.add(child);
}
return offspring;
}
