Titre :

Experiments on greedy and local search heuristics for d–dimensional hypervolume subset selection

Auteur(s) :

Basseur, Matthieu [Auteur]
Laboratoire d'Etudes et de Recherche en Informatique d'Angers [LERIA]
Derbel, Bilel [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Goeffon, Adrien [Auteur]
Laboratoire d'Etudes et de Recherche en Informatique d'Angers [LERIA]
Liefooghe, Arnaud [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]

Titre de la manifestation scientifique :

Genetic and Evolutionary Computation Conference (GECCO 2016)

Ville :

Denver

Pays :

Etats-Unis d'Amérique

Date de début de la manifestation scientifique :

2016

Titre de l’ouvrage :

Genetic and Evolutionary Computation Conference (GECCO 2016)

Éditeur :

ACM Press

Date de publication :

2016

Discipline(s) HAL :

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

Résumé en anglais : [en]

Subset selection constitutes an important stage of any evolutionary multiobjective optimization algorithm when truncating the current approximation set for the next iteration. This appears to be particularly challenging ...
Lire la suite >Subset selection constitutes an important stage of any evolutionary multiobjective optimization algorithm when truncating the current approximation set for the next iteration. This appears to be particularly challenging when the number of solutions to be removed is large, and when the approximation set contains many mutually non-dominating solutions. In particular, indicator-based strategies have been intensively used in recent years for that purpose. However, most solutions for the indicator-based subset selection problem are based on a very simple greedy backward elimination strategy. In this paper, we experiment additional heuristics that include a greedy forward selection and a greedy sequential insertion policies, a first-improvement hill-climbing local search, as well as combinations of those. We evaluate the effectiveness and the efficiency of such heuristics in order to maximize the enclosed hypervolume indicator of candidate subsets during a hypothetical evolutionary process, or as a post-processing phase. Our experimental analysis, conducted on randomly generated as well as structured two-, three- and four-objective mutually non-dominated sets, allows us to appreciate the benefit of these approaches in terms of quality, and to highlight some practical limitations and open challenges in terms of computational resources.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