C++ population::erase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;
     }
 }

void ihs::evolve(population &pop) const
{
	// Let's store some useful variables.
	const problem::base &prob = pop.problem();
	const problem::base::size_type prob_dimension = prob.get_dimension(), prob_i_dimension = prob.get_i_dimension();
	const decision_vector &lb = prob.get_lb(), &ub = prob.get_ub();
	const population::size_type pop_size = pop.size();
	// Get out if there is nothing to do.
	if (pop_size == 0 || m_gen == 0) {
		return;
	}
	decision_vector lu_diff(prob_dimension);
	for (problem::base::size_type i = 0; i < prob_dimension; ++i) {
		lu_diff[i] = ub[i] - lb[i];
	}
	// Int distribution to be used when picking random individuals.
	boost::uniform_int<population::size_type> uni_int(0,pop_size - 1);
	const double c = std::log(m_bw_min/m_bw_max) / m_gen;
	// Temporary individual used during evolution.
	population::individual_type tmp;
	tmp.cur_x.resize(prob_dimension);
	tmp.cur_f.resize(prob.get_f_dimension());
	tmp.cur_c.resize(prob.get_c_dimension());
	for (std::size_t g = 0; g < m_gen; ++g) {
		const double ppar_cur = m_ppar_min + ((m_ppar_max - m_ppar_min) * g) / m_gen, bw_cur = m_bw_max * std::exp(c * g);
		// Continuous part.
		for (problem::base::size_type i = 0; i < prob_dimension - prob_i_dimension; ++i) {
			if (m_drng() < m_phmcr) {
				// tmp's i-th chromosome element is the one from a randomly chosen individual.
				tmp.cur_x[i] = pop.get_individual(uni_int(m_urng)).cur_x[i];
				// Do pitch adjustment with ppar_cur probability.
				if (m_drng() < ppar_cur) {
					// Randomly, add or subtract pitch from the current chromosome element.
					if (m_drng() > .5) {
						tmp.cur_x[i] += m_drng() * bw_cur * lu_diff[i];
					} else {
						tmp.cur_x[i] -= m_drng() * bw_cur * lu_diff[i];
					}
					// Handle the case in which we added or subtracted too much and ended up out
					// of boundaries.
					if (tmp.cur_x[i] > ub[i]) {
						tmp.cur_x[i] = boost::uniform_real<double>(lb[i],ub[i])(m_drng);
					} else if (tmp.cur_x[i] < lb[i]) {
						tmp.cur_x[i] = boost::uniform_real<double>(lb[i],ub[i])(m_drng);
					}
				}
			} else {
				// Pick randomly within the bounds.
				tmp.cur_x[i] = boost::uniform_real<double>(lb[i],ub[i])(m_drng);
			}
		}

		//Integer Part
		for (problem::base::size_type i = prob_dimension - prob_i_dimension; i < prob_dimension; ++i) {
			if (m_drng() < m_phmcr) {
				tmp.cur_x[i] = pop.get_individual(uni_int(m_urng)).cur_x[i];
				if (m_drng() < ppar_cur) {
					if (m_drng() > .5) {
						tmp.cur_x[i] += double_to_int::convert(m_drng() * bw_cur * lu_diff[i]);
					} else {
						tmp.cur_x[i] -= double_to_int::convert(m_drng() * bw_cur * lu_diff[i]);
					}
					// Wrap over in case we went past the bounds.
					if (tmp.cur_x[i] > ub[i]) {
						tmp.cur_x[i] = lb[i] + double_to_int::convert(tmp.cur_x[i] - ub[i]) % static_cast<int>(lu_diff[i]);
					} else if (tmp.cur_x[i] < lb[i]) {
						tmp.cur_x[i] = ub[i] - double_to_int::convert(lb[i] - tmp.cur_x[i]) % static_cast<int>(lu_diff[i]);
					}
				}
			} else {
				// Pick randomly within the bounds.
				tmp.cur_x[i] = boost::uniform_int<int>(lb[i],ub[i])(m_urng);
			}
		}
		// And we push him back
		pop.push_back(tmp.cur_x);
		// We locate the worst individual.
		const population::size_type worst_idx = pop.get_worst_idx();
		// And we get rid of him :)
		pop.erase(worst_idx);
	}
}