Titre :

What makes an instance difficult for black-box 0-1 evolutionary multiobjective optimizers?

Auteur(s) :

Liefooghe, Arnaud [Auteur]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Verel, Sébastien [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Aguirre, Hernan [Auteur]
Faculty of Engineering [Nagano]
Tanaka, Kiyoshi [Auteur]
Faculty of Engineering [Nagano]

Éditeur(s) ou directeur(s) scientifique(s) :

Legrand, Pierrick
Corsini, Marc-Michel
Hao, Jin-Kao
Monmarché, Nicolas
Lutton, Evelyne
Schoenauer, Marc

Titre de la manifestation scientifique :

11th International Conference on Artificial Evolution (EA 2013)

Ville :

Bordeaux

Pays :

France

Date de début de la manifestation scientifique :

2013-10-21

Titre de l’ouvrage :

Artificial Evolution : 11th International Conference, Evolution Artificielle, EA 2013, Bordeaux, France, October 21-23, 2013. Revised Selected Papers

Titre de la revue :

Theoretical Computer Science and General Issues (LNTCS, volume 8752)

Éditeur :

Springer

Date de publication :

2014

Discipline(s) HAL :

Computer Science [cs]/Operations Research [math.OC]

Résumé en anglais : [en]

This paper investigates the correlation between the characteristics extracted from the problem instance and the performance of a simple evolutionary multiobjective optimization algorithm. First, a number of features are ...
Lire la suite >This paper investigates the correlation between the characteristics extracted from the problem instance and the performance of a simple evolutionary multiobjective optimization algorithm. First, a number of features are identified and measured on a large set of enumerable multiobjective NK-landscapes with objective correlation. A correlation analysis is conducted between those attributes, including low-level features extracted from the problem input data as well as high-level features extracted from the Pareto set, the Pareto graph and the fitness landscape. Second, we experimentally analyze the (estimated) running time of the global SEMO algorithm to identify a (1 + ε)-approximation of the Pareto set. By putting this performance measure in relation with problem instance features, we are able to explain the difficulties encountered by the algorithm with respect to the main instance characteristics.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL