void GA::evolve()
{
// create initial population (generation 0)
std::cout << "-- Generation 0 of " << m_generation_max << " ..." << std::endl;
m_generation = 0;
m_population.resize(m_population_size);
m_last_population.resize(m_population_size);
for (unsigned int i=0; i<m_population_size; i++){
m_population[i] = new Individual();
for (auto &var : m_variables) {
m_population[i]->add_cut(get_random_cut(var));
}
}
evaluate_fitness();
show_best();
// loop step until condition is satisfied
while (check_end_condition()) {
m_generation++;
std::cout << "-- Generation " << m_generation << " of " << m_generation_max << " ..." << std::endl;
step();
show_best();
}
std::cout << std::endl;
long total_calc = 1;
for (auto &var : m_variables) {
total_calc *= var.bins;
}
std::cout << "-- Number of calculations = " << hist_z->GetNbins() << " of " << total_calc << std::endl;
output.close();
// make plots
std::cout << "-- Doing some plots..." << std::endl;
plots();
}
void GA::step()
{
pop_vector children;
children.clear();
children.reserve(m_population_size);
// elitism
unsigned int elite_size = m_population_size * m_elitism_rate;
for (unsigned int i=0; i<elite_size; i++) {
children.push_back(m_population[i]->copy());
}
// crossover
while (children.size() < (m_population_size - elite_size)) {
// select parents
int p1 = roulette();
int p2 = roulette();
// crossover parents
crossover(m_population[p1], m_population[p2], children);
}
// mutation
mutate(children);
// update population and save last population
update(children);
// evaluate fitness and sort
evaluate_fitness();
// log, save generation
log();
save_histograms();
}
stResult<TCity> * main_genetic(mySlimTree* SlimTree, TCity * queryObject, stDistance range)
{
time_t begin, end;
begin = time(NULL);
srand(time(NULL));
Population * pop = new_population();
Population * aux_pop = new_empty_population();
infeasible(SlimTree, pop);
set_genotype(pop);
evaluate_fitness(pop, SlimTree, queryObject, range);
#if DEBUG
print_population(pop ,"Pop", number_of_gerations);
#endif
while( (number_of_gerations < MAX_NUMBER_OF_GERATIONS) && (delta_fitness > 0.0001) )
{
number_of_gerations++;
#if DEBUG
cout << "Number of Gerations: "<< number_of_gerations << endl;
#endif
selection_by_tournament(pop, aux_pop);
#if DEBUG
cout << "Torneio" << endl;
#endif
crossover_uniform(aux_pop);
#if DEBUG
cout << "Crossover Uniforme" << endl;
#endif
mutation(aux_pop);
#if DEBUG
cout << "Mutação" << endl;
#endif
set_phenotype(aux_pop);
#if DEBUG
cout << "Fenotipo" << endl;
#endif
infeasible(SlimTree, aux_pop);
#if DEBUG
cout << "Infactibilidade" << endl;
#endif
set_genotype(aux_pop);
#if DEBUG
cout << "Fenotipo" << endl;
#endif
copy_population(aux_pop, pop);
#if DEBUG
cout << "Copiar aux pop" << endl;
#endif
evaluate_fitness(pop, SlimTree, queryObject, range);
#if DEBUG
cout << "Fitness" << endl;
#endif
old_media_of_fitness = media_of_fitness;
media_of_fitness = get_media_of_population(pop);
delta_fitness = fabs(media_of_fitness - old_media_of_fitness);
}
end = time(NULL);
#if DEBUG
print_population(pop, "PopulatioN", number_of_gerations);
print_statistic(pop, begin, end);
#endif
}
