/** {@inheritDoc} */
@Override
public void initialise(Problem problem) {
StructuredType harmony = problem.getDomain().getBuiltRepresenation().getClone();
harmony.randomize(new MersenneTwister());
setCandidateSolution(harmony);
this.getProperties().put(EntityType.FITNESS, InferiorFitness.instance());
}
/**
* Splits up the given {@link OptimisationProblem} into sub-problems, where each sub problem
* contains a sequencial (non-uniform sized) portion of the problem vector, and assigns them to
* all the participating {@link Algorithm}s. This implementation assigns a portion of length
* equals to dimensionality/number of populations + 1 to dimensionality % number of populations of
* the participating algorithms.
*
* @param populations The list of participating {@linkplain PopulationBasedAlgorithm}s.
* @param problem The problem that needs to be re-distributed.
* @param context The context vector maintained by the {@linkplain
* CooperativeCoevolutionAlgorithm}.
*/
public void performDistribution(
List<PopulationBasedAlgorithm> populations, Problem problem, Vector context) {
checkArgument(
populations.size() >= 2,
"There should at least be two Cooperating populations in a Cooperative Algorithm");
int dimension = problem.getDomain().getDimension() / populations.size();
int oddDimensions = problem.getDomain().getDimension() % populations.size();
int i = 0;
int offset = 0;
for (Algorithm population : populations) {
int actualDimension = dimension;
if (i < oddDimensions) actualDimension++;
DimensionAllocation problemAllocation =
new SequencialDimensionAllocation(offset, actualDimension);
population.setOptimisationProblem(
new CooperativeCoevolutionProblemAdapter(problem, problemAllocation, context));
offset += actualDimension;
++i;
}
}
