std::vector<std::pair<population::size_type,std::vector<population::individual_type>::size_type> >
random_r_policy::select(const std::vector<population::individual_type> &immigrants, const population &dest) const
{
const population::size_type rate_limit = std::min<population::size_type>(get_n_individuals(dest),boost::numeric_cast<population::size_type>(immigrants.size()));
// Temporary vectors to store sorted indices of the populations.
std::vector<population::size_type> immigrants_idx(boost::numeric_cast<std::vector<population::size_type>::size_type>(immigrants.size()));
std::vector<population::size_type> dest_idx(boost::numeric_cast<std::vector<population::size_type>::size_type>(dest.size()));
// Fill in the arrays of indices.
iota(immigrants_idx.begin(),immigrants_idx.end(),population::size_type(0));
iota(dest_idx.begin(),dest_idx.end(),population::size_type(0));
// Permute the indices (immigrants).
for (population::size_type i = 0; i < rate_limit; ++i) {
population::size_type next_idx = i + (m_urng() % (rate_limit - i));
if (next_idx != i) {
std::swap(immigrants_idx[i], immigrants_idx[next_idx]);
}
}
// Permute the indices (destination).
for (population::size_type i = 0; i < rate_limit; ++i) {
population::size_type next_idx = i + (m_urng() % (dest.size() - i));
if (next_idx != i) {
std::swap(dest_idx[i], dest_idx[next_idx]);
}
}
// Return value.
std::vector<std::pair<population::size_type,std::vector<population::individual_type>::size_type> >
retval;
for (population::size_type i = 0; i < rate_limit; ++i) {
retval.push_back(std::make_pair(dest_idx[i],immigrants_idx[i]));
}
return retval;
}
std::vector<population::individual_type> best_s_policy::select(const population &pop) const
{
const population::size_type migration_rate = get_n_individuals(pop);
// Create a temporary array of individuals.
std::vector<population::individual_type> result(pop.begin(),pop.end());
// Sort the individuals (best go first).
std::sort(result.begin(),result.end(),dom_comp(pop));
// Leave only desired number of elements in the result.
result.erase(result.begin() + migration_rate,result.end());
return result;
}
std::vector<population::individual_type> best_kill_s_policy::select(population &pop) const
{
pagmo_assert(get_n_individuals(pop) <= pop.size());
// Gets the number of individuals to select
const population::size_type migration_rate = get_n_individuals(pop);
// Create a temporary array of individuals.
std::vector<population::individual_type> result;
// Gets the indexes of the best individuals
std::vector<population::size_type> best_idx = pop.get_best_idx(migration_rate);
// Puts the best individuals in results
for (population::size_type i =0; i< migration_rate; ++i) {
result.push_back(pop.get_individual(best_idx[i]));
}
// Remove them from the original population
// (note: the champion will still carry information on the best guy ...)
for (population::size_type i=0 ; i<migration_rate; ++i) {
pop.reinit(best_idx[i]);
}
return result;
}
// Selection implementation.
std::vector<std::pair<population::size_type,std::vector<population::individual_type>::size_type> >
worst_r_policy::select(const std::vector<population::individual_type> &immigrants, const population &dest) const
{
const population::size_type rate_limit = std::min<population::size_type>(get_n_individuals(dest),boost::numeric_cast<population::size_type>(immigrants.size()));
// Temporary vectors to store sorted indices of the populations.
std::vector<population::size_type> immigrants_idx(boost::numeric_cast<std::vector<population::size_type>::size_type>(immigrants.size()));
std::vector<population::size_type> dest_idx(boost::numeric_cast<std::vector<population::size_type>::size_type>(dest.size()));
// Fill in the arrays of indices.
iota(immigrants_idx.begin(),immigrants_idx.end(),population::size_type(0));
iota(dest_idx.begin(),dest_idx.end(),population::size_type(0));
// Sort the arrays of indices.
// From best to worst.
std::sort(immigrants_idx.begin(),immigrants_idx.end(),indirect_individual_sorter<std::vector<population::individual_type> >(immigrants,dest));
// From worst to best.
std::sort(dest_idx.begin(),dest_idx.end(),indirect_individual_sorter<population>(dest,dest));
std::reverse(dest_idx.begin(),dest_idx.end());
// Create the result.
std::vector<std::pair<population::size_type,std::vector<population::individual_type>::size_type> > result;
for (population::size_type i = 0; i < rate_limit; ++i) {
// Similar to fair policy, but replace unconditionally, without checking if the incoming individuals are better.
result.push_back(std::make_pair(dest_idx[i],immigrants_idx[i]));
}
return result;
}
// Selection implementation.
std::vector<std::pair<population::size_type,std::vector<population::individual_type>::size_type> >
hv_fair_r_policy::select(const std::vector<population::individual_type> &immigrants, const population &dest) const
{
// Fall back to fair_r_policy when facing a single-objective problem.
if (dest.problem().get_f_dimension() == 1) {
return fair_r_policy(m_rate, m_type).select(immigrants, dest);
}
std::vector<population::individual_type> filtered_immigrants;
filtered_immigrants.reserve(immigrants.size());
// Keeps information on the original indexing of immigrants after we filter out the duplicates
std::vector<unsigned int> original_immigrant_indices;
original_immigrant_indices.reserve(immigrants.size());
// Remove the duplicates from the set of immigrants
std::vector<population::individual_type>::iterator im_it = (const_cast<std::vector<population::individual_type> &>(immigrants)).begin();
unsigned int im_idx = 0;
for( ; im_it != immigrants.end() ; ++im_it) {
decision_vector im_x((*im_it).cur_x);
bool equal = true;
for ( unsigned int idx = 0 ; idx < dest.size() ; ++idx ) {
decision_vector isl_x(dest.get_individual(idx).cur_x);
equal = true;
for (unsigned int d_idx = 0 ; d_idx < im_x.size() ; ++d_idx) {
if (im_x[d_idx] != isl_x[d_idx]) {
equal = false;
break;
}
}
if (equal) {
break;
}
}
if (!equal) {
filtered_immigrants.push_back(*im_it);
original_immigrant_indices.push_back(im_idx);
}
++im_idx;
}
// Computes the number of immigrants to be selected (accounting for the destination pop size)
const population::size_type rate_limit = std::min<population::size_type>(get_n_individuals(dest), boost::numeric_cast<population::size_type>(filtered_immigrants.size()));
// Defines the retvalue
std::vector<std::pair<population::size_type, std::vector<population::individual_type>::size_type> > result;
// Skip the remaining computation if there's nothing to do
if (rate_limit == 0) {
return result;
}
// Makes a copy of the destination population
population pop_copy(dest);
// Merge the immigrants to the copy of the destination population
for (population::size_type i = 0; i < rate_limit; ++i) {
pop_copy.push_back(filtered_immigrants[i].cur_x);
}
// Population fronts stored as indices of individuals.
std::vector< std::vector<population::size_type> > fronts_i = pop_copy.compute_pareto_fronts();
// Population fronts stored as fitness vectors of individuals.
std::vector< std::vector<fitness_vector> > fronts_f (fronts_i.size());
// Nadir point is established manually later, first point is a first "safe" candidate.
fitness_vector refpoint(pop_copy.get_individual(0).cur_f);
// Fill fronts_f with fitness vectors and establish the nadir point
for (unsigned int f_idx = 0 ; f_idx < fronts_i.size() ; ++f_idx) {
fronts_f[f_idx].resize(fronts_i[f_idx].size());
for (unsigned int p_idx = 0 ; p_idx < fronts_i[f_idx].size() ; ++p_idx) {
fronts_f[f_idx][p_idx] = fitness_vector(pop_copy.get_individual(fronts_i[f_idx][p_idx]).cur_f);
// Update the nadir point manually for efficiency.
for (unsigned int d_idx = 0 ; d_idx < fronts_f[f_idx][p_idx].size() ; ++d_idx) {
refpoint[d_idx] = std::max(refpoint[d_idx], fronts_f[f_idx][p_idx][d_idx]);
}
}
}
// Epsilon is added to nadir point
for (unsigned int d_idx = 0 ; d_idx < refpoint.size() ; ++d_idx) {
refpoint[d_idx] += m_nadir_eps;
}
// Vector for maintaining the original indices of points for augmented population as 0 and 1
std::vector<unsigned int> g_orig_indices(pop_copy.size(), 1);
unsigned int no_discarded_immigrants = 0;
// Store which front we process (start with the last front) and the number of processed individuals.
unsigned int front_idx = fronts_i.size(); // front_idx is equal to the size, since it's decremented right in the main loop
unsigned int processed_individuals = 0;
// Pairs of (islander index, islander exclusive hypervolume)
// Second item is updated later
std::vector<std::pair<unsigned int, double> > discarded_islanders;
std::vector<std::pair<unsigned int, double> > point_pairs;
// index of currently processed point in the point_pair vector.
// Initiated to its size (=0) in order to enforce the initial computation on penultimate front.
unsigned int current_point = point_pairs.size();
// Stops when we reduce the augmented population to the size of the original population or when the number of discarded islanders reaches the limit
while (processed_individuals < filtered_immigrants.size() && discarded_islanders.size() < rate_limit) {
// if current front was exhausted, load next one
if (current_point == point_pairs.size()) {
--front_idx;
// Compute contributions
std::vector<double> c;
// If there exist a dominated front for front at index front_idx
if (front_idx + 1 < fronts_f.size()) {
std::vector<fitness_vector> merged_front;
// Reserve the memory and copy the fronts
merged_front.reserve(fronts_f[front_idx].size() + fronts_f[front_idx + 1].size());
copy(fronts_f[front_idx].begin(), fronts_f[front_idx].end(), back_inserter(merged_front));
copy(fronts_f[front_idx + 1].begin(), fronts_f[front_idx +1].end(), back_inserter(merged_front));
hypervolume hv(merged_front, false);
c = hv.contributions(refpoint);
} else {
hypervolume hv(fronts_f[front_idx], false);
c = hv.contributions(refpoint);
}
// Initiate the pairs and sort by second item (exclusive volume)
point_pairs.resize(fronts_f[front_idx].size());
for(unsigned int i = 0 ; i < fronts_f[front_idx].size() ; ++i) {
point_pairs[i] = std::make_pair(i, c[i]);
}
current_point = 0;
std::sort(point_pairs.begin(), point_pairs.end(), sort_point_pairs_asc);
}
unsigned int orig_lc_idx = fronts_i[front_idx][point_pairs[current_point].first];
if (orig_lc_idx < dest.size()) {
discarded_islanders.push_back(std::make_pair(orig_lc_idx, 0.0));
} else {
++no_discarded_immigrants;
}
// Flag given individual as discarded
g_orig_indices[orig_lc_idx] = 0;
++processed_individuals;
++current_point;
}
// Number of non-discarded immigrants
unsigned int no_available_immigrants = boost::numeric_cast<unsigned int>(filtered_immigrants.size() - no_discarded_immigrants);
// Pairs of (immigrant index, immigrant exclusive hypervolume)
// Second item is updated later
std::vector<std::pair<unsigned int, double> > available_immigrants;
available_immigrants.reserve(no_available_immigrants);
for(unsigned int idx = dest.size() ; idx < pop_copy.size() ; ++idx) {
// If the immigrant was not discarded add it to the available set
if ( g_orig_indices[idx] == 1 ) {
available_immigrants.push_back(std::make_pair(idx, 0.0));
}
}
// Aggregate all points to establish the hypervolume contribution of available immigrants and discarded islanders
std::vector<fitness_vector> merged_fronts;
merged_fronts.reserve(pop_copy.size());
for(unsigned int idx = 0 ; idx < pop_copy.size() ; ++idx) {
merged_fronts.push_back(pop_copy.get_individual(idx).cur_f);
}
hypervolume hv(merged_fronts, false);
std::vector<std::pair<unsigned int, double> >::iterator it;
for(it = available_immigrants.begin() ; it != available_immigrants.end() ; ++it) {
(*it).second = hv.exclusive((*it).first, refpoint);
}
for(it = discarded_islanders.begin() ; it != discarded_islanders.end() ; ++it) {
(*it).second = hv.exclusive((*it).first, refpoint);
}
// Sort islanders and immigrants according to exclusive hypervolume
sort(available_immigrants.begin(), available_immigrants.end(), hv_fair_r_policy::ind_cmp);
sort(discarded_islanders.begin(), discarded_islanders.end(), hv_fair_r_policy::ind_cmp);
// Number of exchanges is the minimum of the number of non discarded immigrants and the number of discarded islanders
unsigned int no_exchanges = std::min(boost::numeric_cast<unsigned int>(available_immigrants.size()), boost::numeric_cast<unsigned int>(discarded_islanders.size()));
it = available_immigrants.begin();
std::vector<std::pair<unsigned int, double> >::reverse_iterator r_it = discarded_islanders.rbegin();
// Match the best immigrant (forward iterator) with the worst islander (reverse iterator) no_exchanges times.
for(unsigned int i = 0 ; i < no_exchanges ; ++i) {
// Break if any islander is better than an immigrant
if ((*r_it).second > (*it).second) {
break;
}
// Push the pair (islander_idx, fixed_immigrant_idx) to the results
result.push_back(std::make_pair((*r_it).first, original_immigrant_indices[(*it).first - dest.size()]));
++r_it;
++it;
}
return result;
}
std::vector<population::individual_type> hv_greedy_s_policy::select(population &pop) const
{
// Fall back to best_s_policy when facing a single-objective problem.
if (pop.problem().get_f_dimension() == 1) {
return best_s_policy(m_rate, m_type).select(pop);
}
pagmo_assert(get_n_individuals(pop) <= pop.size());
// Gets the number of individuals to select
const population::size_type migration_rate = get_n_individuals(pop);
// Create a temporary array of individuals.
std::vector<population::individual_type> result;
// Indices of fronts.
std::vector< std::vector< population::size_type> > fronts_i = pop.compute_pareto_fronts();
// Fitness vectors of individuals according to the indices above.
std::vector< std::vector< fitness_vector> > fronts_f (fronts_i.size());
// Nadir point is established manually later, first point is as a first "safe" candidate.
fitness_vector refpoint(pop.get_individual(0).cur_f);
for (unsigned int f_idx = 0 ; f_idx < fronts_i.size() ; ++f_idx) {
fronts_f[f_idx].resize(fronts_i[f_idx].size());
for (unsigned int p_idx = 0 ; p_idx < fronts_i[f_idx].size() ; ++p_idx) {
fronts_f[f_idx][p_idx] = fitness_vector(pop.get_individual(fronts_i[f_idx][p_idx]).cur_f);
// Update the nadir point manually for efficiency.
for (unsigned int d_idx = 0 ; d_idx < fronts_f[f_idx][p_idx].size() ; ++d_idx) {
refpoint[d_idx] = std::max(refpoint[d_idx], fronts_f[f_idx][p_idx][d_idx]);
}
}
}
// Epsilon is added to nadir point
for (unsigned int d_idx = 0 ; d_idx < refpoint.size() ; ++d_idx) {
refpoint[d_idx] += m_nadir_eps;
}
// Store which front we process (start with front 0) and the number of processed individuals.
unsigned int front_idx = 0;
unsigned int processed_individuals = 0;
// Vector for maintaining the original indices of points
std::vector<unsigned int> orig_indices;
while (processed_individuals < migration_rate) {
// If we need to pull every point from given front anyway, just push back the individuals right away
if (fronts_f[front_idx].size() <= (migration_rate - processed_individuals)) {
for(unsigned int i = 0 ; i < fronts_i[front_idx].size() ; ++i) {
result.push_back(pop.get_individual(fronts_i[front_idx][i]));
}
processed_individuals += fronts_f[front_idx].size();
++front_idx;
} else {
// Prepare the vector for the original indices
if (orig_indices.size() == 0) {
orig_indices.resize(fronts_i[front_idx].size());
iota(orig_indices.begin(), orig_indices.end(), 0);
}
// Compute the greatest contributor
hypervolume hv(fronts_f[front_idx], false);
hv.set_copy_points(false);
unsigned int gc_idx = hv.greatest_contributor(refpoint);
result.push_back(pop.get_individual(fronts_i[front_idx][orig_indices[gc_idx]]));
// Remove it from the front along with its index
orig_indices.erase(orig_indices.begin() + gc_idx);
fronts_f[front_idx].erase(fronts_f[front_idx].begin() + gc_idx);
++processed_individuals;
}
}
return result;
}
