/** {@inheritDoc} */
@Override
public <T extends Entity> T create(T targetEntity, T current, Topology<T> topology) {
T bestEntity = Topologies.getBestEntity(topology);
List<T> participants =
Selection.copyOf(topology)
.exclude(targetEntity, bestEntity, current)
.orderBy(new RandomArrangement())
.select(Samples.first((int) numberOfDifferenceVectors.getParameter()).unique());
Vector differenceVector = determineDistanceVector(participants);
Vector targetVector =
((Vector) targetEntity.getCandidateSolution())
.multiply(1 - greedynessParameter.getParameter());
Vector bestVector =
((Vector) bestEntity.getCandidateSolution()).multiply(greedynessParameter.getParameter());
Vector trialVector =
bestVector.plus(
targetVector.plus(differenceVector.multiply(scaleParameter.getParameter())));
T trialEntity = (T) current.getClone();
trialEntity.setCandidateSolution(trialVector);
return trialEntity;
}
/**
* Calculate the {@linkplain Vector} that is the resultant of several difference vectors.
*
* @param participants The {@linkplain Entity} list to create the difference vectors from. It is
* very important to note that the {@linkplain Entity} objects within this list should not
* contain duplicates. If duplicates are contained, this will severely reduce the diversity of
* the population as not all entities will be considered.
* @return A {@linkplain Vector} representing the resultant of all calculated difference vectors.
*/
protected Vector determineDistanceVector(List<Entity> participants) {
RandomProvider random = new MersenneTwister();
Vector distanceVector = Vector.fill(0.0, participants.get(0).getCandidateSolution().size());
Iterator<Entity> iterator;
int number = Double.valueOf(this.numberOfDifferenceVectors.getParameter()).intValue();
List<Entity> currentParticipants;
Vector first, second;
double difference;
for (int d = 0; d < distanceVector.size(); d++) {
// get random participants for this dimension
currentParticipants =
(List<Entity>)
Selection.copyOf(participants)
.orderBy(new RandomArrangement(random))
.select(Samples.first(number));
iterator = currentParticipants.iterator();
while (iterator.hasNext()) {
first = (Vector) iterator.next().getCandidateSolution();
second = (Vector) iterator.next().getCandidateSolution();
difference = first.doubleValueOf(d) - second.doubleValueOf(d);
distanceVector.setReal(d, distanceVector.get(d).doubleValue() + difference);
}
}
return distanceVector;
}
/** {@inheritDoc} */
@Override
public <T extends Entity> T create(T targetEntity, T current, fj.data.List<T> topology) {
int number = Double.valueOf(this.numberOfDifferenceVectors.getParameter()).intValue();
List<T> participants =
Selection.copyOf(topology)
.exclude(targetEntity, current)
.orderBy(new RandomArrangement())
.select(Samples.first(number).unique());
Vector differenceVector = determineDistanceVector(participants);
Vector targetVector = (Vector) targetEntity.getCandidateSolution();
Vector trialVector =
targetVector.plus(
differenceVector.multiply(
new P1<Number>() {
@Override
public Number _1() {
return scaleParameter.getParameter();
}
}));
T trialEntity = (T) current.getClone();
trialEntity.setCandidateSolution(trialVector);
return trialEntity;
}
/**
* Reevaluate a specified percentage of the given entities.
*
* @param entities a {@link List} of entities that should be considered for
* reevaluation
* @param reevaluateCount an <code>int<code> specifying how many entities should be
* reevaluated
*/
protected void reevaluate(List<? extends Entity> entities, int reevaluateCount) {
RandomSelector selector = new RandomSelector();
List<? extends Entity> subList = selector.on(entities).select(Samples.first(reevaluateCount));
for (Entity entity : subList) {
// FIXME: does not reevaluate the _best_ positon.
entity.calculateFitness();
}
}
/** {@inheritDoc} */
@Override
public Entity create(Entity targetEntity, Entity current, Topology<? extends Entity> topology) {
List<Entity> participants =
(List<Entity>)
Selection.copyOf(topology).exclude(targetEntity, current).select(Samples.all());
Vector differenceVector = determineDistanceVector(participants);
Vector targetVector = (Vector) targetEntity.getCandidateSolution();
Vector trialVector =
targetVector.plus(
differenceVector.multiply(
new P1<Number>() {
@Override
public Number _1() {
return scaleParameter.getParameter();
}
}));
Entity trialEntity = current.getClone();
trialEntity.setCandidateSolution(trialVector);
return trialEntity;
}
