bool DifferentialEvolution::advanceGeneration(StateP state, DemeP deme)
{
// create donor vectors for each population member
for(uint iIter = 0; iIter < deme->size(); iIter++){
createDonorVectors(deme, state);
}
// perform DE crossover, generate trial vectors (stored in donor_vector)
for(uint iIter = 0; iIter < deme->size(); iIter++) {
crossover(deme, iIter, state);
}
// select the better one for each population member and trial vector
for(uint iIter = 0; iIter < deme->size(); iIter++) {
evaluate(donor_vector[iIter]);
if(donor_vector[iIter]->fitness->isBetterThan(deme->at(iIter)->fitness))
replaceWith(deme->at(iIter), donor_vector[iIter]);
}
//for(uint i = 0; i < deme->size(); i++){
// state->getLogger()->log(5, "deme[" + uint2str(i) + "]: " + dbl2str(deme->at(i)->fitness->getValue()) + "\t" + uint2str(deme->at(i)->index));
//}
donor_vector.clear();
return true;
}
bool RandomSearch :: advanceGeneration(StateP state, DemeP deme)
{
// initialize and evaluate all individuals
for(uint i = 0; i < deme->size(); i++) {
deme->at(i)->initialize(state);
evaluate(deme->at(i));
}
return true;
}
