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);
}
}
}
}
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 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 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 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 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++;
}
}
// 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
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;
}
}
void crossover_function(population& p)
{
population new_population;
std::vector<int> cross_set;
std::vector<int>::iterator it;
float rd;
// choose which specimen will crossover
for(int i=0; i<population_size; i++)
{
rd = (randid()*1.0f) / (RAND_MAX*1.0f*population_size);
if(rd > p[i].adapt)
cross_set.push_back(i);
}
// we want even number of parents
if(cross_set.size() & 1)
cross_set.pop_back();
// more randomness
random_shuffle ( cross_set.begin(), cross_set.end(), p_randgenid );
it = cross_set.begin();
// crossover each pair from left to right
while( it != cross_set.end())
{
std::pair<permutation,permutation> desc;
crossover::type ctype;
if(cfg.compare_operators)
{
switch(randid() % xop_count)
{
case 0: ctype = crossover::type::OX; break;
case 1: ctype = crossover::type::CX; break;
case 2:
default: ctype = crossover::type::PMX; break;
}
desc = crossover::random_crossover(ctype, p[*it].perm, p[*(it+1)].perm);
}
else
{
desc = crossover::random_crossover(cfg.crossover_type, p[*it].perm, p[*(it+1)].perm);
}
specimen ch1, ch2;
ch1.perm = desc.first;
ch1.eval = evaluation(ch1.perm);
ch1.adapt = 0.0;
ch2.perm = desc.second;
ch2.eval = evaluation(ch2.perm);
ch2.adapt = 0.0;
if(cfg.compare_operators && ( ((p[*it].eval + p[*(it+1)].eval) / (ch1.eval + ch2.eval)) >= 1 ) )
{
switch(ctype)
{
case crossover::type::OX: ox_count++; break;
case crossover::type::CX: cx_count++; break;
case crossover::type::PMX:
default: pmx_count++; break;
}
}
it++; it++;
x_count++;
new_population.push_back(ch1);
new_population.push_back(ch2);
}
p.insert(p.end(), new_population.begin(), new_population.end());
}