sea.cpp

/*****************************************************************************
 *   Copyright (C) 2004-2015 The PaGMO development team,                     *
 *   Advanced Concepts Team (ACT), European Space Agency (ESA)               *
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 *   https://github.com/esa/pagmo                                            *
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 *   act@esa.int                                                             *
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#include <boost/random/uniform_int.hpp>
#include <string>
#include <vector>
#include <algorithm>

#include "../exceptions.h"
#include "../population.h"
#include "../types.h"
#include "base.h"
#include "sea.h"

namespace pagmo { namespace algorithm {


sea::sea(int gen)
        :base(),m_gen(gen)
{
        if (gen < 0) {
                pagmo_throw(value_error,"number of generations must be nonnegative");
        }
}

base_ptr sea::clone() const
{
        return base_ptr(new sea(*this));
}


void sea::evolve(population &pop) const
{
        // Let's store some useful variables.
        const problem::base &prob = pop.problem();
        const problem::base::size_type Di = prob.get_i_dimension();
        const decision_vector &lb = prob.get_lb(), &ub = prob.get_ub();

        //We perform some checks to determine wether the problem/population are suitable for SGA
        if ( prob.get_c_dimension() != 0 ) {
                pagmo_throw(value_error,"The problem is not box constrained and EA is not suitable to solve it");
        }

        if ( prob.get_dimension() - prob.get_i_dimension() != 0 ) {
                pagmo_throw(value_error,"The problem has a continuous dimension and this (N+1)-EA Simple Evolutionary Algorithm is not suitable to solve it");
        }

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

        int new_gene;
        // Main loop
        for (int j = 0; j<m_gen; j++) {

                // Offspring is generated from the best individual
                decision_vector offspring = pop.get_individual(pop.get_best_idx()).cur_x;

                // Mutation of the best individual. Each gene is flipped with probability 1/Di to a different value
                for (pagmo::problem::base::size_type j = 0; j < Di;++j) {//for each integer variable
                        if (m_drng() < 1.0/Di) {
                                do {
                                        new_gene = boost::uniform_int<int>(lb[j],ub[j])(m_urng);
                                } while(new_gene == offspring[j]);
                                offspring[j] = new_gene;
                        }
                }
                
                // We add the mutated individual to the population (this will also evaluate its fitness)
                pop.push_back(offspring);
                // We get rid of the worst individual (in multi-objective this is computed using
                // the crowding distance operator)
                pop.erase(pop.get_worst_idx());

        } // end of main loop
}

std::string sea::get_name() const
{
        return "(N+1)-EA Simple Evolutionary Algorithm";
}


std::string sea::human_readable_extra() const
{
        std::ostringstream s;
        s << "gen:" << m_gen << ' ';
        
        return s.str();
}

}} //namespaces

BOOST_CLASS_EXPORT_IMPLEMENT(pagmo::algorithm::sea)