Interval-based Initialization Method for Permutation-based Problems

Malika Mehdi 1 Nouredine Melab 1 El-Ghazali Talbi 1 Bouvry Pascal
1 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
Abstract : When dealing with exponential search spaces and when no special knowledge is available on global optima, initial populations for population-based meta-heuristics should be uniformly distributed on the search space in order to sample basins of attraction of all local optima. In this paper, we propose a new initialization strategy for permutation problems. The new method is based on an original tree representation of the search space. Such representation was previously used for exact methods but never for meta-heuristics. The proposed method has been tested using a parallel Genetic Algorithm implemented in the ParadisEO framework and experimented on the Nationwide Grid5000 experimental grid using the Q3AP (3D QAP) permutation problem. The preliminary results are promising.
Type de document :
Communication dans un congrès
IEEE Congress on Evolutionary Computation, Jul 2010, Barcelona, Spain. 2010, IEEE Congress on Evolutionary Computation. 〈10.1109/CEC.2010.5586526〉
Liste complète des métadonnées

https://hal.inria.fr/inria-00524216
Contributeur : Malika Mehdi <>
Soumis le : jeudi 7 octobre 2010 - 11:55:12
Dernière modification le : jeudi 11 janvier 2018 - 06:22:13

Identifiants

Citation

Malika Mehdi, Nouredine Melab, El-Ghazali Talbi, Bouvry Pascal. Interval-based Initialization Method for Permutation-based Problems. IEEE Congress on Evolutionary Computation, Jul 2010, Barcelona, Spain. 2010, IEEE Congress on Evolutionary Computation. 〈10.1109/CEC.2010.5586526〉. 〈inria-00524216〉

Partager

Métriques

Consultations de la notice

232