Exemplo n.º 1
0
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);
}
Exemplo n.º 2
0
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);
}
Exemplo n.º 3
0
/**
 * 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;
	
}
Exemplo n.º 4
0
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
}