C++ population::clear方法代码示例

 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;
     }
 }

//.........这里部分代码省略.........
	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]) {

/**
 * Run the CORE algorithm
 *
 * @param[in,out] pop input/output pagmo::population to be evolved.
 */
void cstrs_core::evolve(population &pop) const
{	
	// store 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 CORE
	if(prob_c_dimension < 1) {
		pagmo_throw(value_error,"The problem is not constrained and CORE is not suitable to solve it");
	}
	if(prob.get_f_dimension() != 1) {
		pagmo_throw(value_error,"The problem is multiobjective and CORE is not suitable to solve it");
	}

	// Get out if there is nothing to do.
	if(pop_size == 0) {
		return;
	}

	// generates the unconstrained problem
	problem::con2uncon prob_unconstrained(prob);

	// associates the population to this problem
	population pop_uncon(prob_unconstrained);

	// fill this unconstrained population
	pop_uncon.clear();
	for(population::size_type i=0; i<pop_size; i++) {
		pop_uncon.push_back(pop.get_individual(i).cur_x);
	}

	// vector containing the infeasibles positions
	std::vector<population::size_type> pop_infeasibles;

	// Main CORE loop
	for(int k=0; k<m_gen; k++) {

		if(k%m_repair_frequency == 0) {
			pop_infeasibles.clear();

			// get the infeasible individuals
			for(population::size_type i=0; i<pop_size; i++) {
				if(!prob.feasibility_c(pop.get_individual(i).cur_c)) {
					pop_infeasibles.push_back(i);
				}
			}

			// random shuffle of infeasibles?
			population::size_type number_of_repair = (population::size_type)(m_repair_ratio * pop_infeasibles.size());

			// repair the infeasible individuals
			for(population::size_type i=0; i<number_of_repair; i++) {
				const population::size_type &current_individual_idx = pop_infeasibles.at(i);

                pop.repair(current_individual_idx, m_repair_algo);
			}

			// the population is repaired, it can be now used in the new unconstrained population
			// only the repaired individuals are put back in the population
			for(population::size_type i=0; i<number_of_repair; i++) {
				population::size_type current_individual_idx = pop_infeasibles.at(i);
				pop_uncon.set_x(current_individual_idx, pop.get_individual(current_individual_idx).cur_x);
			}
		}

		m_original_algo->evolve(pop_uncon);

		// push back the population in the main problem
		pop.clear();
		for(population::size_type i=0; i<pop_size; i++) {
			pop.push_back(pop_uncon.get_individual(i).cur_x);
		}

		// Check the exit conditions (every 40 generations, just as DE)
		if(k % 40 == 0) {
			decision_vector tmp(prob_dimension);

			double dx = 0;
			for(decision_vector::size_type i=0; i<prob_dimension; i++) {
				tmp[i] = pop.get_individual(pop.get_worst_idx()).best_x[i] - pop.get_individual(pop.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(pop.get_individual(pop.get_worst_idx()).best_f[0] - pop.get_individual(pop.get_best_idx()).best_f[0]);
//.........这里部分代码省略.........