Title :

Decomposition-based multi-objective landscape features and automated algorithm selection

Author(s) :

Cosson, Raphaël [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Derbel, Bilel [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Liefooghe, Arnaud [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Optimisation de grande taille et calcul large échelle [BONUS]
Aguirre, Hernán [Auteur]
Faculty of Engineering [Nagano]
Tanaka, Kiyoshi [Auteur]
Faculty of Engineering [Nagano]
Zhang, Qingfu [Auteur]
City University of Hong Kong [Hong Kong] [CUHK]

Conference title :

EvoCOP 2021 - 21st European Conference on Evolutionary Computation in Combinatorial Optimization

City :

Virtual Event

Country :

Espagne

Start date of the conference :

2021

Publication date :

2021-03-27

HAL domain(s) :

Informatique [cs]/Recherche opérationnelle [cs.RO]
Mathématiques [math]/Optimisation et contrôle [math.OC]

English abstract : [en]

Landscape analysis is of fundamental interest for improving our understanding on the behavior of evolutionary search, and for developing general-purpose automated solvers based on techniques from statistics and machine ...
Show more >Landscape analysis is of fundamental interest for improving our understanding on the behavior of evolutionary search, and for developing general-purpose automated solvers based on techniques from statistics and machine learning. In this paper, we push a step towards the development of a landscape-aware approach by proposing a set of landscape features for multi-objective combinatorial optimization, by decomposing the original multi-objective problem into a set of single-objective sub-problems. Based on a comprehensive set of bi-objective Open image in new window and three variants of the state-of-the-art MOEA/D algorithm, we study the association between the proposed features, the global properties of the considered landscapes, and algorithm performance. We also show that decomposition-based features can be integrated into an automated approach for predicting algorithm performance and selecting the most accurate one on blind instances. In particular, our study reveals that such a landscape-aware approach is substantially better than the single best solver computed over the three considered MOEA/D variants.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

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

Source :

Harvested from HAL