void reduce_population_through_ranking(GeneWrapper * population_after, int population_size_after,
GeneWrapper * population_before, int population_size_before,
GeneWrapper * base_population, int base_population_size)
{
char buffer[51];
int i = population_size_after - 1;
int j = population_size_before - 1;
GeneWrapper * population_before_copy = NULL;
copy_population(&population_before_copy, population_before, population_size_before);
rank_compete(population_before_copy, population_size_before, base_population, base_population_size);
while (i >= 0) {
NewStringFromGene(population_before_copy[j].gene, buffer);
SetGeneFromString(buffer, population_after[i].gene);
i--;
j--;
}
free_population(population_before_copy, population_size_before);
}
void reduce_population_through_competition(GeneWrapper * initial_population, GeneWrapper * new_population,
int initial_population_size, int new_population_size) {
char buffer[51];
int i = initial_population_size - 1;
int j = new_population_size - 1;
GeneWrapper * new_population_copy = NULL;
copy_population(&new_population_copy, new_population, new_population_size);
mutual_compete(new_population_size, new_population_copy);
while (i >= 0) {
NewStringFromGene(new_population_copy[j].gene, buffer);
SetGeneFromString(buffer, initial_population[i].gene);
i--;
j--;
}
free_population(new_population_copy, new_population_size);
}
/**
* Reproduction phase
**/
vector<vector<tower>*>* reproduction_iteration(const int nb_children, const float mutation_prob, vector<vector<tower>*>* population, vector<tower>* towers, const int dist, float taux_var)
{
vector<vector<tower>*>* remaining_parents = copy_population(population); // copy of the population
vector<vector<tower>*>* children = new vector<vector<tower>*>(); // allocate vector of children
vector<tower>* child = new vector<tower>();
int index_1, index_2;
//int max_children = min(nb_children, (int) population->size() / 2); // if nb children > population / 2 can't generate enough children
for (int i = 0; i < min(nb_children, (int)remaining_parents->size() / 2); i++) {
do {
do {
index_1 = aliased_select(remaining_parents); // use the heuristic
index_2 = aliased_select(remaining_parents);
} while (index_2 == index_1);
child = mix((*remaining_parents)[index_1], (*remaining_parents)[index_2], dist, towers, taux_var); // check on the remaining_parents
} while(child->empty());
if (static_cast <float> (rand()) / static_cast <float> (RAND_MAX) < mutation_prob) {
mutate(child, towers, dist, taux_var);
}
children->push_back(child);
if (index_1 < index_2) {
remaining_parents->erase(remaining_parents->begin() + index_1);
remaining_parents->erase(remaining_parents->begin() + index_2 - 1);
}
else {
remaining_parents->erase(remaining_parents->begin() + index_2);
remaining_parents->erase(remaining_parents->begin() + index_1 - 1);
}
}
for (vector<vector<tower>*>::iterator it = remaining_parents->begin(); it != remaining_parents->end(); it++) {
delete *it;
}
delete remaining_parents;
return children;
}
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
}
