// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*- //----------------------------------------------------------------------------- // moeoEntropyMetric.h // (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2006 /* This library... Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr */ //----------------------------------------------------------------------------- #ifndef MOEOENTROPYMETRIC_H_ #define MOEOENTROPYMETRIC_H_ #include /** * The entropy gives an idea of the diversity of a Pareto set relatively to another Pareto set * * (Basseur, Seynhaeve, Talbi: 'Design of Multi-objective Evolutionary Algorithms: Application to the Flow-shop Scheduling Problem', in Proc. of the 2002 Congress on Evolutionary Computation, IEEE Press, pp. 1155-1156) */ template < class EOT > class moeoEntropyMetric:public moeoVectorVsVectorBM < EOT, double > { public: /** * The fitness type of a solution */ typedef typename EOT::Fitness EOFitness; /** * Returns the entropy of the Pareto set '_set1' relatively to the Pareto set '_set2' * @param _set1 the first Pareto set * @param _set2 the second Pareto set */ double operator () (const std::vector < EOFitness > &_set1, const std::vector < EOFitness > &_set2) { // normalization std::vector < EOFitness > set1 = _set1; std::vector < EOFitness > set2 = _set2; removeDominated (set1); removeDominated (set2); prenormalize (set1); normalize (set1); normalize (set2); // making of PO* std::vector < EOFitness > star; // rotf :-) computeUnion (set1, set2, star); removeDominated (star); // making of PO1 U PO* std::vector < EOFitness > union_set1_star; // rotf again ... computeUnion (set1, star, union_set1_star); unsigned C = union_set1_star.size (); float omega = 0; float entropy = 0; for (unsigned i = 0; i < C; i++) { unsigned N_i = howManyInNicheOf (union_set1_star, union_set1_star[i], star.size ()); unsigned n_i = howManyInNicheOf (set1, union_set1_star[i], star.size ()); if (n_i > 0) { omega += 1.0 / N_i; entropy += (float) n_i / (N_i * C) * log (((float) n_i / C) / log (2.0)); } } entropy /= -log (omega); entropy *= log (2.0); return entropy; } private: std::vector < double >vect_min_val; std::vector < double >vect_max_val; void removeDominated (std::vector < EOFitness > &_f) { for (unsigned i = 0; i < _f.size (); i++) { bool dom = false; for (unsigned j = 0; j < _f.size (); j++) if (i != j && _f[j].dominates (_f[i])) { dom = true; break; } if (dom) { _f[i] = _f.back (); _f.pop_back (); i--; } } } void prenormalize (const std::vector < EOFitness > &_f) { vect_min_val.clear (); vect_max_val.clear (); for (unsigned char i = 0; i < EOFitness::fitness_traits::nObjectives (); i++) { float min_val = _f.front ()[i], max_val = min_val; for (unsigned j = 1; j < _f.size (); j++) { if (_f[j][i] < min_val) min_val = _f[j][i]; if (_f[j][i] > max_val) max_val = _f[j][i]; } vect_min_val.push_back (min_val); vect_max_val.push_back (max_val); } } void normalize (std::vector < EOFitness > &_f) { for (unsigned i = 0; i < EOFitness::fitness_traits::nObjectives (); i++) for (unsigned j = 0; j < _f.size (); j++) _f[j][i] = (_f[j][i] - vect_min_val[i]) / (vect_max_val[i] - vect_min_val[i]); } void computeUnion (const std::vector < EOFitness > &_f1, const std::vector < EOFitness > &_f2, std::vector < EOFitness > &_f) { _f = _f1; for (unsigned i = 0; i < _f2.size (); i++) { bool b = false; for (unsigned j = 0; j < _f1.size (); j++) if (_f1[j] == _f2[i]) { b = true; break; } if (!b) _f.push_back (_f2[i]); } } unsigned howManyInNicheOf (const std::vector < EOFitness > &_f, const EOFitness & _s, unsigned _size) { unsigned n = 0; for (unsigned i = 0; i < _f.size (); i++) { if (euclidianDistance (_f[i], _s) < (_s.size () / (double) _size)) n++; } return n; } double euclidianDistance (const EOFitness & _set1, const EOFitness & _to, unsigned _deg = 2) { double dist = 0; for (unsigned i = 0; i < _set1.size (); i++) dist += pow (fabs (_set1[i] - _to[i]), (int) _deg); return pow (dist, 1.0 / _deg); } }; #endif /*MOEOENTROPYMETRIC_H_ */