• English
    • français
  • Help
  •  | 
  • Contact
  •  | 
  • About
  •  | 
  • Login
  • HAL portal
  •  | 
  • Pages Pro
  • EN
  •  / 
  • FR
View Item 
  •   LillOA Home
  • Liste des unités
  • Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
  • View Item
  •   LillOA Home
  • Liste des unités
  • Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Decomposition-based multi-objective landscape ...
  • BibTeX
  • CSV
  • Excel
  • RIS

Document type :
Communication dans un congrès avec actes
DOI :
10.1007/978-3-030-72904-2_3
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] refId
Optimisation de grande taille et calcul large échelle [BONUS]
Liefooghe, Arnaud [Auteur] refId
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
Université de Lille

Mentions légales
Université de Lille © 2017