population random_breeder::breed(const population &generation, const std::vector<double> &fitnesses) const
{
using namespace std;
population ret;
tree_crossover_reproducer repr;
node_crossover_reproducer mrepr;
builder random_builder;
experimental_parameters e = exp_params();
const uint32_t growth_tree_min = e["growth_tree_min"];
const uint32_t growth_tree_max = e["growth_tree_max"];
while(ret.size() < generation.size())
{
const agent &mother = generation[rand() % generation.size()];
const agent &father = generation[rand() % generation.size()];
agent mutant = random_builder.grow(program_types_set, growth_tree_min, growth_tree_max);
const vector<agent> children = repr.reproduce(vector<agent> { mother, father });
if(children.size() > 0)
{
agent res0 = mrepr.reproduce(vector<agent> { children[0], mutant })[0];
ret.push_back(res0);
}
if(children.size() > 1)
{
agent res1 = mrepr.reproduce(vector<agent> { children[1], mutant })[0];
ret.push_back(res1);
}
}
return ret;
}
void report(population& p)
{
switch(cfg.report_every)
{
case config::report::none:
break;
case config::report::avg:
{
float avg = 0.0;
for(auto i = p.begin(); i != p.end(); ++i)
avg += i->eval;
avg /= static_cast<float>(p.size());
std::cout << iter << ' ' << avg << '\n';
}
break;
case config::report::best:
std::cout << iter << ' ' << p[0].eval << '\n';
break;
}
if(cfg.report_var)
{
std::cout << iter << ' ' << statistics::variance(p) << '\n';
}
}
void increase_tree_depth(generation_table& gtable, population& pop, int& from_depth, combo::arity_t& needed_arg_count, int fill_from_arg, const reduct::rule& reduction_rule) {
for (population::iterator it = pop.begin(); it != pop.end(); ++it) {
int number_of_combinations = 1;
bool can_have_leaves = false;
std::vector<generation_table::iterator> assignment;
std::vector<combo::combo_tree::leaf_iterator> leaves;
for (combo::combo_tree::leaf_iterator lit = it->begin_leaf(); lit != it->end_leaf(); ++lit) {
if (combo::is_argument(*lit))
if (combo::get_argument(*lit).abs_idx() <= needed_arg_count)
continue;
for (std::vector<generation_node>::iterator it2 = gtable.begin(); it2 != gtable.end(); it2++)
if (combo::equal_type_tree(it2->node, combo::infer_vertex_type(*it, lit))) {
if (it2->glist.size() != 0)
can_have_leaves = true;
assignment.push_back(it2);
number_of_combinations *= it2->glist.size();
break;
}
}
if (!can_have_leaves)
number_of_combinations = 0;
for (int i = 0; i < number_of_combinations; i++) {
combo::combo_tree temp_tree(*it);
int ongoing_count = 1;
leaves.clear();
for (combo::combo_tree::leaf_iterator lit = temp_tree.begin_leaf(); lit != temp_tree.end_leaf(); ++lit)
leaves.push_back(lit);
std::vector<generation_table::iterator>::iterator it2 = assignment.begin();
for (std::vector<combo::combo_tree::leaf_iterator>::iterator lit = leaves.begin(); lit != leaves.end(); ++lit) {
if (combo::is_argument(**lit))
if (combo::get_argument(**lit).abs_idx() <= needed_arg_count)
continue;
if ((*it2)->glist.size() != 0) {
node_list::iterator it3 = (*it2)->glist.begin();
for (int n = 0; n < (int)(i/(number_of_combinations/(ongoing_count * (*it2)->glist.size())) % (*it2)->glist.size()); n++)
it3++;
temp_tree.replace(*lit, *it3);
ongoing_count *= (*it2)->glist.size();
}
it2++;
}
bool erased = true;
for (combo::combo_tree::leaf_iterator lit = temp_tree.begin_leaf(); lit != temp_tree.end_leaf(); ++lit) {
if (combo::get_arity(*lit) != 0 && !combo::is_argument(*lit)) {
erased = false;
break;
}
}
if (combo::does_contain_all_arg_up_to(temp_tree, needed_arg_count)) {
erased = false;
}
if (!erased) {
//Uses less memory but is very slow
/*fill_leaves_single(temp_tree, fill_from_arg);
reduced_insertion(new_pop, temp_tree, reduction_rule);*/
pop.push_front(temp_tree);
}
}
}
}
family_vector rnd_selector::select(const population&p,const int fcount)
{
family_vector result(fcount);
for(int i=0;i<fcount;i++)
result[i]=std::make_pair(m_random.uniform(0,p.size()-1),
m_random.uniform(0,p.size()-1));
return result;
}
void adapt_population(population& p)
{
float F_min = min_element(p.begin(),p.end(),eval_comp)->eval;
for(unsigned int i=0; i<p.size(); i++)
{
float sum = 0.0;
for(unsigned int j=0; j<p.size(); j++)
sum += p[j].eval - F_min;
p[i].adapt = (p[i].eval - F_min)/sum;
}
}
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;
}
void fill_leaves(population& pop, int& from_arg) {
for (population::iterator it = pop.begin(); it != pop.end(); ++it) {
int local_from_arg = from_arg;
for (combo::combo_tree::leaf_iterator lit = it->begin_leaf(); lit != it->end_leaf(); ++lit) {
if (!combo::is_argument(*lit)) {
combo::arity_t number_of_leaves = combo::get_arity(*lit);
if (number_of_leaves < 0) number_of_leaves = -1 * number_of_leaves + 1;
for (combo::arity_t count = 0; count < number_of_leaves; count++) {
(*it).append_child(lit, combo::argument(++local_from_arg));
}
}
}
}
}
void ms::evolve(population &pop) const
{
// Let's store some useful variables.
const population::size_type NP = pop.size();
// Get out if there is nothing to do.
if (m_starts == 0 || NP == 0) {
return;
}
// Local population used in the algorithm iterations.
population working_pop(pop);
//ms main loop
for (int i=0; i< m_starts; ++i)
{
working_pop.reinit();
m_algorithm->evolve(working_pop);
if (working_pop.problem().compare_fc(working_pop.get_individual(working_pop.get_best_idx()).cur_f,working_pop.get_individual(working_pop.get_best_idx()).cur_c,
pop.get_individual(pop.get_worst_idx()).cur_f,pop.get_individual(pop.get_worst_idx()).cur_c
) )
{
//update best population replacing its worst individual with the good one just produced.
pop.set_x(pop.get_worst_idx(),working_pop.get_individual(working_pop.get_best_idx()).cur_x);
pop.set_v(pop.get_worst_idx(),working_pop.get_individual(working_pop.get_best_idx()).cur_v);
}
if (m_screen_output)
{
std::cout << i << ". " << "\tCurrent iteration best: " << working_pop.get_individual(working_pop.get_best_idx()).cur_f << "\tOverall champion: " << pop.champion().f << std::endl;
}
}
}
void mutation_function(population& p)
{
for(auto i = p.begin(); i != p.end(); ++i)
{
float prob = 1 - i->adapt; // probability of mutation
float r = uniform_random(); // random float between <0,1)
if(r < prob)
{
mutation::random_transposition(i->perm);
i->eval = evaluation(i->perm); // after mutation is done we have to evaluate this specimen again
}
}
}
void replacement(population& p)
{
std::sort(p.begin(), p.end(), eval_cmp());
p.resize(population_size);
if( p[0].eval < best_specimen.eval )
{
best_specimen = p[0];
deviate_count = 0;
}
else
{
deviate_count++;
}
}
void allocate_pop(population &pop,size_t NumDims,int stra_num,int num_obj)
{
size_t pop_size=pop.size();
size_t i;
for ( i=0;i<pop_size;i++ )
allocate_ind(pop[i],NumDims,stra_num,num_obj);
}
void reallocate_stra(population &pop,int stra_idx,int size,double val)
{
int pop_size=pop.size();
int i;
for ( i=0;i<pop_size;i++ )
pop[i].stra[stra_idx].assign(size,val);
}
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;
}
/**
* @param[in] pop input population.
*
* @return the number of individuals to be migrated from/to the population according to the current migration rate and type.
*/
population::size_type base::get_n_individuals(const population &pop) const
{
population::size_type retval = 0;
switch (m_type) {
case absolute:
retval = boost::numeric_cast<population::size_type>(m_rate);
if (retval > pop.size()) {
pagmo_throw(value_error,"absolute migration rate is higher than population size");
}
break;
case fractional:
retval = boost::numeric_cast<population::size_type>(double_to_int::convert(m_rate * pop.size()));
pagmo_assert(retval <= pop.size());
}
return retval;
}
void report_end(population& p)
{
if(cfg.report_every == config::report::none)
{
if(cfg.report_population)
{
if(cfg.debug)
{
std::cout << "Reporting population\n";
std::cout << "Evaluation = [ permutation ]\n";
}
std::sort(p.begin(), p.end(), eval_cmp());
for(auto i = p.begin(); i != p.end(); ++i)
std::cout << i->eval << " = " << i->perm << std::endl;
}
if(cfg.report_best)
{
if(cfg.debug)
{
std::cout << "Reporting best speciman\n";
std::cout << "Evaluation = [ permutation ]\n";
}
std::cout << best_specimen.eval << " = " << best_specimen.perm << std::endl;
}
if(cfg.optimum > -1)
{
if(cfg.debug)
{
std::cout << "Reporting number of iterations needed to compute best specimen with given optimum value or --max-iter if optimum is not reached\n";
}
std::cout << iter << "\n";
}
if(cfg.compare_operators)
{
std::cout << "ox = " << ox_count << "\n";
std::cout << "cx = " << cx_count << "\n";
std::cout << "pmx = " << pmx_count << "\n";
std::cout << "crossovers = " << x_count << "\n";
}
}
}
void init_prev_population()
{
for(int i = 0; i < population_size; ++i)
{
specimen s;
s.perm = permutation(N);
s.eval = s.adapt = 0.0;
prev_population.push_back(s);
}
}
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;
}
void reallocate_stra(population &pop,population &trial_pop,int stra_idx,int size,double val)
{
int pop_size=pop.size();
int tri_pop_size=trial_pop.size();
if ( pop_size!=tri_pop_size )
{
stringstream ss_err;
ss_err<<"\n"
<<"Error in reallocate_stra:"
<<"pop and trial_pop's size mismatch."
<<"\n";
throw logic_error(ss_err.str());
}
int i;
for ( i=0;i<pop_size;i++ )
{
pop[i].stra[stra_idx].assign(size,val);
trial_pop[i].stra[stra_idx].assign(size,val);
}
}
/// Evolve method.
void monte_carlo::evolve(population &pop) const
{
// Let's store some useful variables.
const problem::base &prob = pop.problem();
const problem::base::size_type prob_dimension = prob.get_dimension(), prob_i_dimension = prob.get_i_dimension();
const decision_vector &lb = prob.get_lb(), &ub = prob.get_ub();
const population::size_type pop_size = pop.size();
// Get out if there is nothing to do.
if (pop_size == 0 || m_max_eval == 0) {
return;
}
// Initialise temporary decision vector, fitness vector and decision vector.
decision_vector tmp_x(prob_dimension);
fitness_vector tmp_f(prob.get_f_dimension());
constraint_vector tmp_c(prob.get_c_dimension());
// Main loop.
for (std::size_t i = 0; i < m_max_eval; ++i) {
// Generate a random decision vector.
for (problem::base::size_type j = 0; j < prob_dimension - prob_i_dimension; ++j) {
tmp_x[j] = boost::uniform_real<double>(lb[j],ub[j])(m_drng);
}
for (problem::base::size_type j = prob_dimension - prob_i_dimension; j < prob_dimension; ++j) {
tmp_x[j] = boost::uniform_int<int>(lb[j],ub[j])(m_urng);
}
// Compute fitness and constraints.
prob.objfun(tmp_f,tmp_x);
prob.compute_constraints(tmp_c,tmp_x);
// Locate the worst individual.
const population::size_type worst_idx = pop.get_worst_idx();
if (prob.compare_fc(tmp_f,tmp_c,pop.get_individual(worst_idx).cur_f,pop.get_individual(worst_idx).cur_c)) {
pop.set_x(worst_idx,tmp_x);
}
}
}
// perform (\lambda+\mu) -> (\lambda) selection
void dgea_alg::select(population& pop, population& child_pop)
{
int pop_size=pop.size();
int num_dims=m_ppara->get_dim();
int i;
population pop_merged(pop_size);
vector<vector<double> > prev_x(pop_size);
for ( i=0;i<pop_size;i++ )
{
prev_x[i].resize(num_dims);
pop_merged[i]=pop[i];
// record previous x
prev_x[i]=pop[i].x;
}// for every individual
// push child population at the tail of merged population
copy( child_pop.begin(),child_pop.end(),back_inserter(pop_merged) );
nth_element(pop_merged.begin(), pop_merged.begin()+pop_size, pop_merged.end());
//// heap sorting
//make_heap(pop_merged.begin(),pop_merged.end());
//for ( i=0;i<pop_size;i++ )
//{
// // record delta x for stat purpose
// pop_heap(pop_merged.begin(),pop_merged.end());
// pop_merged.pop_back();
// const individual& new_ind=pop_merged.front();
// pop[i]=new_ind;
// m_alg_stat.delta_x[i]=pop[i].x-prev_x[i];// pop_merged:{parents,offsprings}
//}
for ( i=0;i<pop_size;i++ )
{
pop[i]=pop_merged[i];
// record delta x
m_alg_stat.delta_x[i] = (pop[i].x-prev_x[i]);
}// for every individual
}// end function select
/**
* Updates the constraints scaling vector with the given population.
* @param[in] population pop.
*/
void cstrs_self_adaptive::update_c_scaling(const population &pop)
{
if(*m_original_problem != pop.problem()) {
pagmo_throw(value_error,"The problem linked to the population is not the same as the problem given in argument.");
}
// Let's store some useful variables.
const population::size_type pop_size = pop.size();
// get the constraints dimension
//constraint_vector c(m_original_problem->get_c_dimension(), 0.);
problem::base::c_size_type prob_c_dimension = m_original_problem->get_c_dimension();
problem::base::c_size_type number_of_eq_constraints =
m_original_problem->get_c_dimension() -
m_original_problem->get_ic_dimension();
const std::vector<double> &c_tol = m_original_problem->get_c_tol();
m_c_scaling.resize(m_original_problem->get_c_dimension());
std::fill(m_c_scaling.begin(),m_c_scaling.end(),0.);
// evaluates the scaling factor
for(population::size_type i=0; i<pop_size; i++) {
// updates the current constraint vector
const population::individual_type ¤t_individual = pop.get_individual(i);
const constraint_vector &c = current_individual.cur_c;
// computes scaling with the right definition of the constraints (can be in base problem? currently used
// by con2mo as well)
for(problem::base::c_size_type j=0; j<number_of_eq_constraints; j++) {
m_c_scaling[j] = std::max(m_c_scaling[j], std::max(0., (std::abs(c.at(j)) - c_tol.at(j))) );
}
for(problem::base::c_size_type j=number_of_eq_constraints; j<prob_c_dimension; j++) {
m_c_scaling[j] = std::max(m_c_scaling[j], std::max(0., c.at(j) - c_tol.at(j)) );
}
}
}
void enumerate_program_trees(generation_table& gtable, int depth, combo::type_tree& ttree, population& pop, const reduct::rule& reduction_rule) {
pop.clear();
// For each generation node with the right return-type, add it to the pop
for (std::vector<generation_node>::iterator it = gtable.begin(); it != gtable.end(); ++it) {
if (combo::equal_type_tree(it->node, combo::get_signature_output(ttree))) {
for (node_list::iterator it2 = it->glist.begin(); it2 != it->glist.end(); it2++)
pop.push_back(combo::combo_tree(*it2));
break;
}
}
// add the right number of arguments
int from_arg = combo::get_signature_inputs(ttree).size();
combo::arity_t needed_arg_count = combo::type_tree_arity(ttree);
std::cout << ttree << " " << needed_arg_count << std::endl;
for (int i = 1; i < depth; i++) {
fill_leaves(pop, from_arg);
reduce(pop, reduction_rule);
increase_tree_depth(gtable, pop, i, needed_arg_count, from_arg, reduction_rule);
}
for (population::iterator it = pop.begin(); it != pop.end();) {
bool erased = false;
for (combo::combo_tree::leaf_iterator lit = it->begin_leaf(); lit != it->end_leaf(); ++lit) {
if (get_arity(*lit) != 0 && !combo::is_argument(*lit)) {
erased = true;
break;
}
}
if (!combo::does_contain_all_arg_up_to(*it, needed_arg_count)) {
erased = true;
}
if (erased)
it = pop.erase(it);
else
++it;
}
}
// tournament selection without replacement
// the individuals that survive reproduction are placed in the mating pool
void tselect_without_replacement( population &pop )
{
int *shuffle = new int [pop.popsize()];
int pick;
int s = parameter::tournament_size;
pre_tselect_without_replacement( shuffle, pick, pop.popsize() );//take pick to zero & shuffle the shuffle array (permutation)
pop.MatingPool[0]=pop.best();
for( int i=1; i< pop.popsize(); i++ )
{
if( pick+s > pop.popsize() )
pre_tselect_without_replacement( shuffle, pick, pop.popsize() );
pop.MatingPool[i] = pop.tournament_winner( shuffle, pick, s );
}
delete [] shuffle;
}
family_vector niche_ga::select(const population&p,int count)
{
dna_cmeans::u_vector data=fill_data_vector(p);
int cluster_count=static_cast<int>(p.size()*0.10);
dna_cmeans::result r;
if(call_nums>=10){
call_nums=0;
cm.m_center_sequence.erase(cm.m_center_sequence.begin(),cm.m_center_sequence.end());
}
if(cm.m_center_sequence.size()==0){
r=cm.cluster(dna_distance,cluster_count,data.begin(),data.end(),data.size(),1.0);
}
else{
r=cm.cluster(dna_distance,data.begin(),data.end(),data.size());
}
call_nums++;
clusters cls=dna_cmeans::u2clsuters(r.u);
Random rnd;
family_vector result(count);
int c=0; // сколько мы уже отобрали для скрещивания
while(c!=count){
int father_cluster=rnd.uniform(0,cls.size()-1);
if(cls[father_cluster].size()<2)
continue;
int father_index=rnd.uniform(0,cls[father_cluster].size()-1);
int mather_cluster=father_cluster;
int mather_index=rnd.uniform(0,cls[mather_cluster].size()-1);
result[c]=std::make_pair(cls[father_cluster][father_index].first,
cls[mather_cluster][mather_index].first);
c++;
}
return result;
}
bool simpleLSM_job::eval_population_fitness(population &pop)
{
char pwd[FILENAME_MAX];
getcwd(pwd, sizeof(pwd));
log.debug("Current working directory", pwd);
float cputime=0;
float walltime=0;
for (sample &s : pop)
{
prepare_work_dir_for_sample(s);
boost::timer::cpu_timer timer;
eval_sample_fitness(s);
boost::timer::cpu_times elapsed = timer.elapsed();
log.debug(" CPU TIME: ", (elapsed.user + elapsed.system) / 1e9, " seconds", ", WALLCLOCK TIME: ", elapsed.wall / 1e9, " seconds");
cputime+=((elapsed.user + elapsed.system) / 1e9);
walltime+=(elapsed.wall / 1e9);
}
log.debug("total CPU TIME:", cputime, "total WALLCLOCK TIME:", walltime);
log.debug("avergae CPU TIME:", cputime/pop.size(), "avergae WALLCLOCK TIME:", walltime/pop.size());
return true;
}
/**
* Constructor using initial constrained problem
*
* Note: This problem is not inteded to be used by itself. Instead use the
* self-adaptive algorithm if you want to solve constrained problems.
*
* @param[in] problem base::problem to be modified to use a self-adaptive
* as constraints handling technique.
* @param[in] pop population to be used to set up the penalty coefficients.
*
*/
cstrs_self_adaptive::cstrs_self_adaptive(const base &problem, const population &pop):
base_meta(
problem,
problem.get_dimension(),
problem.get_i_dimension(),
problem.get_f_dimension(),
0,
0,
std::vector<double>()),
m_apply_penalty_1(false),
m_scaling_factor(0.0),
m_c_scaling(problem.get_c_dimension(),0.0),
m_f_hat_down(problem.get_f_dimension(),0.0),
m_f_hat_up(problem.get_f_dimension(),0.0),
m_f_hat_round(problem.get_f_dimension(),0.0),
m_i_hat_down(0.0),
m_i_hat_up(0.0),
m_i_hat_round(0.0),
m_map_fitness(),
m_map_constraint(),
m_decision_vector_hash()
{
if(m_original_problem->get_c_dimension() <= 0){
pagmo_throw(value_error,"The original problem has no constraints.");
}
// check that the dimension of the problem is 1
if (m_original_problem->get_f_dimension() != 1) {
pagmo_throw(value_error,"The original fitness dimension of the problem must be one, multi objective problems can't be handled with self adaptive meta problem.");
}
if(problem != pop.problem()) {
pagmo_throw(value_error,"The problem linked to the population is not the same as the problem given in argument.");
}
update_penalty_coeff(pop);
}
void erase_duplicate(population& pop) {
pop.sort(opencog::size_tree_order<combo::vertex>());
pop.unique();
}
population choosiness_breeder::breed(const population &generation, const std::vector<double> &fitnesses) const
{
using namespace std;
population ret;
tree_crossover_reproducer repr;
node_crossover_reproducer mrepr;
builder random_builder;
deque<const agent *> mut_gen;
for(const auto &a : generation) mut_gen.push_back(&a);
experimental_parameters e = exp_params();
const uint32_t growth_tree_min = e["growth_tree_min"];
const uint32_t growth_tree_max = e["growth_tree_max"];
const double max_dist = sqrt(_maze.rows() * _maze.rows() + _maze.columns() * _maze.columns());
_phenotypes.clear();
for(const auto &a : mut_gen) _phenotypes.at(a->final_state().row, a->final_state().col).push_back(a);
map<const agent *, vector<const agent *> > matches;
for(auto ait = mut_gen.begin(); ait != mut_gen.end();)
{
const agent *const a = *ait;
const vector<vector<const agent *> > plausible_mates =
_phenotypes.find_in_range(a->final_state().row, a->final_state().col,
a->chromosomes()[0]);
vector<const agent *> flattened_mates;
for(const auto &v : plausible_mates) flattened_mates.insert(flattened_mates.end(), v.begin(), v.end());
for(auto it = flattened_mates.begin(); it != flattened_mates.end();)
{
if(distance((*it)->final_state().col, (*it)->final_state().row,
a->final_state().col, a->final_state().row) > (*it)->chromosomes()[0])
{
it = flattened_mates.erase(it);
continue;
}
++it;
}
if(flattened_mates.empty())
{
ait = mut_gen.erase(ait);
continue;
}
matches[a] = flattened_mates;
++ait;
}
const population::size_type new_size = generation.size();
random_shuffle(mut_gen.begin(), mut_gen.end());
while(ret.size() < new_size)
{
for(const auto &mother : mut_gen)
{
if(ret.size() >= new_size) break;
const vector<const agent *> &fathers = matches[mother];
if(fathers.empty()) continue;
const agent &father = *fathers[rand() % fathers.size()];
agent mutant = random_builder.grow(program_types_set, growth_tree_min, growth_tree_max);
mutant.set_chromosomes(vector<double> { rand_normal() * max_dist });
vector<agent> children = repr.reproduce(vector<agent> { *mother, father });
if(children.empty()) continue;
agent &child = children[0];
child.set_chromosomes(vector<double> { (mother->chromosomes()[0] + father.chromosomes()[0]) / 2.0 });
agent res = mrepr.reproduce(vector<agent> { child, mutant })[0];
res.set_chromosomes(vector<double> { (child.chromosomes()[0] + mutant.chromosomes()[0]) / 2.0 });
ret.push_back(res);
}
}
return ret;
}
/**
* Run the co-evolution algorithm
*
* @param[in,out] pop input/output pagmo::population to be evolved.
*/
void cstrs_co_evolution::evolve(population &pop) const
{
// Let's store some useful variables.
const problem::base &prob = pop.problem();
const population::size_type pop_size = pop.size();
const problem::base::size_type prob_dimension = prob.get_dimension();
// get the constraints dimension
problem::base::c_size_type prob_c_dimension = prob.get_c_dimension();
//We perform some checks to determine wether the problem/population are suitable for co-evolution
if(prob_c_dimension < 1) {
pagmo_throw(value_error,"The problem is not constrained and co-evolution is not suitable to solve it");
}
if(prob.get_f_dimension() != 1) {
pagmo_throw(value_error,"The problem is multiobjective and co-evolution is not suitable to solve it");
}
// Get out if there is nothing to do.
if(pop_size == 0) {
return;
}
//get the dimension of the chromosome of P2
unsigned int pop_2_dim = 0;
switch(m_method)
{
case algorithm::cstrs_co_evolution::SIMPLE:
{
pop_2_dim = 2;
break;
}
case algorithm::cstrs_co_evolution::SPLIT_NEQ_EQ:
{
pop_2_dim = 4;
break;
}
case algorithm::cstrs_co_evolution::SPLIT_CONSTRAINTS:
pop_2_dim = 2*prob.get_c_dimension();
break;
default:
pagmo_throw(value_error,"The constraints co-evolutionary method is not valid.");
break;
}
// split the population into two populations
// the population P1 associated to the modified problem with penalized fitness
// and population P2 encoding the penalty weights
// Populations size
population::size_type pop_1_size = pop_size;
population::size_type pop_2_size = m_pop_penalties_size;
//Creates problem associated to P2
problem::cstrs_co_evolution_penalty prob_2(prob,pop_2_dim,pop_2_size);
prob_2.set_bounds(m_pen_lower_bound,m_pen_upper_bound);
//random initialization of the P2 chromosome (needed for the fist generation)
std::vector<decision_vector> pop_2_x(pop_2_size);
std::vector<fitness_vector> pop_2_f(pop_2_size);
for(population::size_type j=0; j<pop_2_size; j++) {
pop_2_x[j] = decision_vector(pop_2_dim,0.);
// choose random coefficients between lower bound and upper bound
for(population::size_type i=0; i<pop_2_dim;i++) {
pop_2_x[j][i] = boost::uniform_real<double>(m_pen_lower_bound,m_pen_upper_bound)(m_drng);
}
}
//vector of the population P1. Initialized with clones of the original population
std::vector<population> pop_1_vector;
for(population::size_type i=0; i<pop_2_size; i++){
pop_1_vector.push_back(population(pop));
}
// Main Co-Evolution loop
for(int k=0; k<m_gen; k++) {
// for each individuals of pop 2, evolve the current population,
// and store the position of the feasible idx
for(population::size_type j=0; j<pop_2_size; j++) {
problem::cstrs_co_evolution prob_1(prob, pop_1_vector.at(j), m_method);
// modify the problem by setting decision vector encoding penalty
// coefficients w1 and w2 in prob 1
prob_1.set_penalty_coeff(pop_2_x.at(j));
// creating the POPULATION 1 instance based on the
// updated prob 1
// prob_1 is a BASE_META???? THE CLONE OF prob_1 IN POP_1 IS AT THE LEVEL OF
// THE BASE CLASS AND NOT AT THE LEVEL OF THE BASE_META, NO?!?
population pop_1(prob_1,0);
// initialize P1 chromosomes. The fitnesses related to problem 1 are computed
for(population::size_type i=0; i<pop_1_size; i++) {
pop_1.push_back(pop_1_vector.at(j).get_individual(i).cur_x);
}
// evolve the P1 instance
m_original_algo->evolve(pop_1);
//updating the original problem population (computation of fitness and constraints)
pop_1_vector.at(j).clear();
for(population::size_type i=0; i<pop_1_size; i++){
pop_1_vector.at(j).push_back(pop_1.get_individual(i).cur_x);
}
// set up penalization variables needs for the population 2
// the constraints has not been evaluated yet.
prob_2.update_penalty_coeff(j,pop_2_x.at(j),pop_1_vector.at(j));
}
// creating the POPULATION 2 instance based on the
// updated prob 2
population pop_2(prob_2,0);
// compute the fitness values of the second population
for(population::size_type i=0; i<pop_2_size; i++) {
pop_2.push_back(pop_2_x[i]);
}
m_original_algo_penalties->evolve(pop_2);
// store the new chromosomes
for(population::size_type i=0; i<pop_2_size; i++) {
pop_2_x[i] = pop_2.get_individual(i).cur_x;
pop_2_f[i] = pop_2.get_individual(i).cur_f;
}
// Check the exit conditions (every 40 generations, just as DE)
if(k % 40 == 0) {
// finds the best population
population::size_type best_idx = 0;
for(population::size_type j=1; j<pop_2_size; j++) {
if(pop_2_f[j][0] < pop_2_f[best_idx][0]) {
best_idx = j;
}
}
const population ¤t_population = pop_1_vector.at(best_idx);
decision_vector tmp(prob_dimension);
double dx = 0;
for(decision_vector::size_type i=0; i<prob_dimension; i++) {
tmp[i] = current_population.get_individual(current_population.get_worst_idx()).best_x[i] -
current_population.get_individual(current_population.get_best_idx()).best_x[i];
dx += std::fabs(tmp[i]);
}
if(dx < m_xtol ) {
if (m_screen_output) {
std::cout << "Exit condition -- xtol < " << m_xtol << std::endl;
}
break;
}
double mah = std::fabs(current_population.get_individual(current_population.get_worst_idx()).best_f[0] -
current_population.get_individual(current_population.get_best_idx()).best_f[0]);
if(mah < m_ftol) {
if(m_screen_output) {
std::cout << "Exit condition -- ftol < " << m_ftol << std::endl;
}
break;
}
// outputs current values
if(m_screen_output) {
std::cout << "Generation " << k << " ***" << std::endl;
std::cout << " Best global fitness: " << current_population.champion().f << std::endl;
std::cout << " xtol: " << dx << ", ftol: " << mah << std::endl;
std::cout << " xtol: " << dx << ", ftol: " << mah << std::endl;
}
}
}
// find the best fitness population in the final pop
population::size_type best_idx = 0;
for(population::size_type j=1; j<pop_2_size; j++) {
if(pop_2_f[j][0] < pop_2_f[best_idx][0]) {
best_idx = j;
}
}
// store the final population in the main population
// can't avoid to recompute the vectors here, otherwise we
// clone the problem stored in the population with the
// pop = operator!
pop.clear();
for(population::size_type i=0; i<pop_1_size; i++) {
pop.push_back(pop_1_vector.at(best_idx).get_individual(i).cur_x);
}
}
/**
* Updates the penalty coefficients with the given population.
* @param[in] population pop.
*/
void cstrs_self_adaptive::update_penalty_coeff(const population &pop)
{
if(*m_original_problem != pop.problem()) {
pagmo_throw(value_error,"The problem linked to the population is not the same as the problem given in argument.");
}
// Let's store some useful variables.
const population::size_type pop_size = pop.size();
// Get out if there is nothing to do.
if (pop_size == 0) {
return;
}
m_map_fitness.clear();
m_map_constraint.clear();
// store f and c in maps depending on the the x hash
for(population::size_type i=0; i<pop_size; i++) {
const population::individual_type ¤t_individual = pop.get_individual(i);
// m_map_fitness.insert(std::pair<std::size_t, fitness_vector>(m_decision_vector_hash(current_individual.cur_x),current_individual.cur_f));
m_map_fitness[m_decision_vector_hash(current_individual.cur_x)]=current_individual.cur_f;
m_map_constraint[m_decision_vector_hash(current_individual.cur_x)]=current_individual.cur_c;
}
std::vector<population::size_type> feasible_idx(0);
std::vector<population::size_type> infeasible_idx(0);
// store indexes of feasible and non feasible individuals
for(population::size_type i=0; i<pop_size; i++) {
const population::individual_type ¤t_individual = pop.get_individual(i);
if(m_original_problem->feasibility_c(current_individual.cur_c)) {
feasible_idx.push_back(i);
} else {
infeasible_idx.push_back(i);
}
}
// if the population is only feasible, then nothing is done
if(infeasible_idx.size() == 0) {
update_c_scaling(pop);
m_apply_penalty_1 = false;
m_scaling_factor = 0.;
return;
}
m_apply_penalty_1 = false;
m_scaling_factor = 0.;
// updates the c_scaling, needed for solution infeasibility computation
update_c_scaling(pop);
// evaluate solutions infeasibility
//compute_pop_solution_infeasibility(solution_infeasibility, pop);
std::vector<double> solution_infeasibility(pop_size);
std::fill(solution_infeasibility.begin(),solution_infeasibility.end(),0.);
// evaluate solutions infeasibility
solution_infeasibility.resize(pop_size);
std::fill(solution_infeasibility.begin(),solution_infeasibility.end(),0.);
for(population::size_type i=0; i<pop_size; i++) {
const population::individual_type ¤t_individual = pop.get_individual(i);
// compute the infeasibility of the constraint
solution_infeasibility[i] = compute_solution_infeasibility(current_individual.cur_c);
}
// search position of x_hat_down, x_hat_up and x_hat_round
population::size_type hat_down_idx = -1;
population::size_type hat_up_idx = -1;
population::size_type hat_round_idx = -1;
// first case, the population contains at least one feasible solution
if(feasible_idx.size() > 0) {
// initialize hat_down_idx
hat_down_idx = feasible_idx.at(0);
// x_hat_down = feasible individual with lowest objective value in p
for(population::size_type i=0; i<feasible_idx.size(); i++) {
const population::size_type current_idx = feasible_idx.at(i);
const population::individual_type ¤t_individual = pop.get_individual(current_idx);
if(m_original_problem->compare_fitness(current_individual.cur_f, pop.get_individual(hat_down_idx).cur_f)) {
hat_down_idx = current_idx;
}
}
// hat down is now available
fitness_vector f_hat_down = pop.get_individual(hat_down_idx).cur_f;
// x_hat_up value depends if the population contains infeasible individual with objective
// function better than f_hat_down
bool pop_contains_infeasible_f_better_x_hat_down = false;
for(population::size_type i=0; i<infeasible_idx.size(); i++) {
const population::size_type current_idx = infeasible_idx.at(i);
const population::individual_type ¤t_individual = pop.get_individual(current_idx);
if(m_original_problem->compare_fitness(current_individual.cur_f, f_hat_down)) {
pop_contains_infeasible_f_better_x_hat_down = true;
// initialize hat_up_idx
hat_up_idx = current_idx;
break;
}
}
if(pop_contains_infeasible_f_better_x_hat_down) {
// hat_up_idx is already initizalized
// gets the individual with maximum infeasibility and objfun lower than f_hat_down
for(population::size_type i=0; i<infeasible_idx.size(); i++) {
const population::size_type current_idx = infeasible_idx.at(i);
const population::individual_type ¤t_individual = pop.get_individual(current_idx);
if(m_original_problem->compare_fitness(current_individual.cur_f, f_hat_down) &&
(solution_infeasibility.at(current_idx) >= solution_infeasibility.at(hat_up_idx)) ) {
if(solution_infeasibility.at(current_idx) == solution_infeasibility.at(hat_up_idx)) {
if(m_original_problem->compare_fitness(current_individual.cur_f, pop.get_individual(hat_up_idx).cur_f)) {
hat_up_idx = current_idx;
}
} else {
hat_up_idx = current_idx;
}
}
}
// apply penalty 1
m_apply_penalty_1 = true;
} else {
// all the infeasible soutions have an objective function value greater than f_hat_down
// the worst is the one that has the maximum infeasibility
// initialize hat_up_idx
hat_up_idx = infeasible_idx.at(0);
for(population::size_type i=0; i<infeasible_idx.size(); i++) {
const population::size_type current_idx = infeasible_idx.at(i);
const population::individual_type ¤t_individual = pop.get_individual(current_idx);
if(solution_infeasibility.at(current_idx) >= solution_infeasibility.at(hat_up_idx)) {
if(solution_infeasibility.at(current_idx) == solution_infeasibility.at(hat_up_idx)) {
if(m_original_problem->compare_fitness(pop.get_individual(hat_up_idx).cur_f, current_individual.cur_f)) {
hat_up_idx = current_idx;
}
} else {
hat_up_idx = current_idx;
}
}
}
// do not apply penalty 1
m_apply_penalty_1 = false;
}
} else { // case where there is no feasible solution in the population
// best is the individual with the lowest infeasibility
hat_down_idx = 0;
hat_up_idx = 0;
for(population::size_type i=0; i<pop_size; i++) {
const population::individual_type ¤t_individual = pop.get_individual(i);
if(solution_infeasibility.at(i) <= solution_infeasibility.at(hat_down_idx)) {
if(solution_infeasibility.at(i) == solution_infeasibility.at(hat_down_idx)) {
if(m_original_problem->compare_fitness(current_individual.cur_f, pop.get_individual(hat_down_idx).cur_f)) {
hat_down_idx = i;
}
} else {
hat_down_idx = i;
}
}
}
// worst individual
for(population::size_type i=0; i<pop_size; i++) {
const population::individual_type ¤t_individual = pop.get_individual(i);
if(solution_infeasibility.at(i) >= solution_infeasibility.at(hat_up_idx)) {
if(solution_infeasibility.at(i) == solution_infeasibility.at(hat_up_idx)) {
if(m_original_problem->compare_fitness(pop.get_individual(hat_up_idx).cur_f, current_individual.cur_f)) {
hat_up_idx = i;
}
} else {
hat_up_idx = i;
}
}
}
// apply penalty 1 to the population
m_apply_penalty_1 = true;
}
// stores the hat round idx, i.e. the solution with highest objective
// function value in the population
hat_round_idx = 0;
for(population::size_type i=0; i<pop_size; i++) {
const population::individual_type ¤t_individual = pop.get_individual(i);
if(m_original_problem->compare_fitness(pop.get_individual(hat_round_idx).cur_f, current_individual.cur_f)) {
hat_round_idx = i;
}
}
// get the objective function values of the three individuals
m_f_hat_round = pop.get_individual(hat_round_idx).cur_f;
m_f_hat_down = pop.get_individual(hat_down_idx).cur_f;
m_f_hat_up = pop.get_individual(hat_up_idx).cur_f;
// get the solution infeasibility values of the three individuals
m_i_hat_round = solution_infeasibility.at(hat_round_idx);
m_i_hat_down = solution_infeasibility.at(hat_down_idx);
m_i_hat_up = solution_infeasibility.at(hat_up_idx);
// computes the scaling factor
m_scaling_factor = 0.;
// evaluates scaling factor
if(m_original_problem->compare_fitness(m_f_hat_down, m_f_hat_up)) {
m_scaling_factor = (m_f_hat_round[0] - m_f_hat_up[0]) / m_f_hat_up[0];
} else {
m_scaling_factor = (m_f_hat_round[0] - m_f_hat_down[0]) / m_f_hat_down[0];
}
if(m_f_hat_up[0] == m_f_hat_round[0]) {
m_scaling_factor = 0.;
}
}
