/**
* In DOVS, we provide the algorithm with a start individual from file, this
* start individual is the start search point of the DOVS algorithm. We use
* this start point to construct a hyperbox contains promising solutions,
* and sample from this region, the number of sample is equal to parameter
* "pop.subpop.X.size" in parameter files.
*
* However, due to redundant samples, we the individuals size may be
* smaller than what have been specified in pop.subpop.X.size.
*/
public override Population InitialPopulation(IEvolutionState state, int thread)
{
Population p = base.InitialPopulation(state, thread);
// make sure the each subpop only have one individual
for (int i = 0; i < p.Subpops.Count; i++)
{
if (p.Subpops[i].Species is DOVSSpecies)
{
DOVSSpecies species = (DOVSSpecies)p.Subpops[i].Species;
if (p.Subpops[i].Individuals.Count != 1)
{
state.Output.Fatal("contain more than one start point");
}
// add the start point to the visited ArrayList
species.Visited.Clear();
species.Visited.Add(p.Subpops[i].Individuals[0]);
species.VisitedIndexMap[p.Subpops[i].Individuals[0]] = 0;
species.OptimalIndex = 0;
IntegerVectorIndividual ind = (IntegerVectorIndividual)species.Visited[species.OptimalIndex];
// For the visited solution, record its coordinate
// positions in the multimap
for (int j = 0; j < species.GenomeSize; ++j)
{
// The individual is the content. The key is its
// coordinate position
species.Corners[j].Insert(ind.genome[j], ind);
}
// update MPA
species.UpdateMostPromisingArea(state);
// sample from MPA
int initialSize = p.Subpops[i].InitialSize;
IList <Individual> candidates = species.MostPromisingAreaSamples(state, initialSize);
// get unique candidates for evaluation, this is Sk in paper
IList <Individual> uniqueCandidates = species.UniqueSamples(state, candidates);
// update the individuals
p.Subpops[i].Individuals = uniqueCandidates;
}
}
return(p);
}
/**
* This method have three major part, first identify the best indiviudal,
* and then call updateMostPromisingArea(...) to construct a hyperbox around
* this individual. At last, sampled a new population from the hyperbox and
* take the none redundant samples and return it.
*/
public override Population BreedPopulation(IEvolutionState state)
{
Population pop = state.Population;
for (int i = 0; i < pop.Subpops.Count; i++)
{
Subpopulation subpop = pop.Subpops[i];
if (!(subpop.Species is DOVSSpecies)) // uh oh
{
state.Output.Fatal("To use DOVSBreeder, subpopulation " + i
+ " must contain a DOVSSpecies. But it contains a " + subpop.Species);
}
DOVSSpecies species = (DOVSSpecies)(subpop.Species);
// we assume backTrackingTest is always false.
// Thus we combine activeSolution and Sk (individuals) to
// identify the optimal
species.FindBestSample(state, subpop);
// Right now activeSolutions only has A_{k-1}, need to combine S_k
for (int j = 0; j < subpop.Individuals.Count; j++)
{
species.ActiveSolutions.Add(subpop.Individuals[i]);
}
// Ak and bk will have all the constraints, including original
// problem formulation and MPR
// A b are original problem formulation constraints
// activeSolutions will then have the indices for those solutions
// already visited and define MPR
// excluding current best solution
// update MPA
species.UpdateMostPromisingArea(state);
// sample from MPA
IList <Individual> candidates = species.MostPromisingAreaSamples(state, subpop.InitialSize);
// get Sk for evaluation
IList <Individual> Sk = species.UniqueSamples(state, candidates);
// update the individuals
subpop.Individuals = Sk;
}
return(pop);
}
