/* * * Copyright (C) DOLPHIN Project-Team, INRIA Futurs, 2006-2007 * (C) OPAC Team, LIFL, 2002-2007 * * Arnaud Liefooghe * * This software is governed by the CeCILL license under French law and * abiding by the rules of distribution of free software. You can use, * modify and/ or redistribute the software under the terms of the CeCILL * license as circulated by CEA, CNRS and INRIA at the following URL * "http://www.cecill.info". * * As a counterpart to the access to the source code and rights to copy, * modify and redistribute granted by the license, users are provided only * with a limited warranty and the software's author, the holder of the * economic rights, and the successive licensors have only limited liability. * * In this respect, the user's attention is drawn to the risks associated * with loading, using, modifying and/or developing or reproducing the * software by the user in light of its specific status of free software, * that may mean that it is complicated to manipulate, and that also * therefore means that it is reserved for developers and experienced * professionals having in-depth computer knowledge. Users are therefore * encouraged to load and test the software's suitability as regards their * requirements in conditions enabling the security of their systems and/or * data to be ensured and, more generally, to use and operate it in the * same conditions as regards security. * The fact that you are presently reading this means that you have had * knowledge of the CeCILL license and that you accept its terms. * * ParadisEO WebSite : http://paradiseo.gforge.inria.fr * Contact: paradiseo-help@lists.gforge.inria.fr * */ //----------------------------------------------------------------------------- #ifndef MOEOCROWDINGDIVERSITYASSIGNMENT_H_ #define MOEOCROWDINGDIVERSITYASSIGNMENT_H_ #include #include #include /** * Diversity assignment sheme based on crowding proposed in: * K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, "A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II", IEEE Transactions on Evolutionary Computation, vol. 6, no. 2 (2002). */ template < class MOEOT > class moeoCrowdingDiversityAssignment : public moeoDiversityAssignment < MOEOT > { public: /** the objective vector type of the solutions */ typedef typename MOEOT::ObjectiveVector ObjectiveVector; /** * Returns a big value (regarded as infinite) */ double inf() const { return std::numeric_limits::max(); } /** * Returns a very small value that can be used to avoid extreme cases (where the min bound == the max bound) */ double tiny() const { return 1e-6; } /** * Computes diversity values for every solution contained in the population _pop * @param _pop the population */ void operator()(eoPop < MOEOT > & _pop) { if (_pop.size() <= 2) { for (unsigned int i=0; i<_pop.size(); i++) { _pop[i].diversity(inf()); } } else { setDistances(_pop); } } /** * @warning NOT IMPLEMENTED, DO NOTHING ! * Updates the diversity values of the whole population _pop by taking the deletion of the objective vector _objVec into account. * @param _pop the population * @param _objVec the objective vector * @warning NOT IMPLEMENTED, DO NOTHING ! */ void updateByDeleting(eoPop < MOEOT > & /*_pop*/, ObjectiveVector & /*_objVec*/) { std::cout << "WARNING : updateByDeleting not implemented in moeoCrowdingDiversityAssignment" << std::endl; } protected: /** * Sets the distance values * @param _pop the population */ virtual void setDistances (eoPop < MOEOT > & _pop) { double min, max, distance; unsigned int nObjectives = MOEOT::ObjectiveVector::nObjectives(); // set diversity to 0 for (unsigned int i=0; i<_pop.size(); i++) { _pop[i].diversity(0.0); } // for each objective for (unsigned int obj=0; obj objComp(obj); // sort std::sort(_pop.begin(), _pop.end(), objComp); // min & max min = _pop[0].objectiveVector()[obj]; max = _pop[_pop.size()-1].objectiveVector()[obj]; // set the diversity value to infiny for min and max _pop[0].diversity(inf()); _pop[_pop.size()-1].diversity(inf()); for (unsigned int i=1; i<_pop.size()-1; i++) { distance = (_pop[i+1].objectiveVector()[obj] - _pop[i-1].objectiveVector()[obj]) / (max-min); _pop[i].diversity(_pop[i].diversity() + distance); } } } }; #endif /*MOEOCROWDINGDIVERSITYASSIGNMENT_H_*/