Preventing premature convergence and proving the optimality in evolutionary algorithms

Abstract : Evolutionary Algorithms (EA) usually carry out an efficient exploration of the search-space, but get often trapped in local minima and do not prove the optimality of the solution. Interval-based techniques, on the other hand, yield a numerical proof of optimality of the solution. However, they may fail to converge within a reasonable time due to their inability to quickly compute a good approximation of the global minimum and their exponential complexity. The contribution of this paper is a hybrid algorithm called Charibde in which a particular EA, Differential Evolution, cooperates with a Branch and Bound algorithm endowed with interval propagation techniques. It prevents premature convergence toward local optima and outperforms both deterministic and stochastic existing approaches. We demonstrate its efficiency on a benchmark of highly multimodal problems, for which we provide previously unknown global minima and certification of optimality.
Type de document :
Communication dans un congrès
EA 2013, 11th International Conference on Artificial Evolution, Oct 2013, Bordeaux, France. pp 84-94 ; ISBN : 9782953926736, 2013
Liste complète des métadonnées


https://hal.archives-ouvertes.fr/hal-00880716
Contributeur : Charlie Vanaret <>
Soumis le : jeudi 7 novembre 2013 - 01:22:37
Dernière modification le : mardi 22 mars 2016 - 01:28:26
Document(s) archivé(s) le : samedi 8 février 2014 - 04:35:28

Fichier

ea2013.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-00880716, version 1

Citation

Charlie Vanaret, Jean-Baptiste Gotteland, Nicolas Durand, Jean-Marc Alliot. Preventing premature convergence and proving the optimality in evolutionary algorithms. EA 2013, 11th International Conference on Artificial Evolution, Oct 2013, Bordeaux, France. pp 84-94 ; ISBN : 9782953926736, 2013. <hal-00880716>

Partager

Métriques

Consultations de
la notice

293

Téléchargements du document

228