Title :

Surrogate-assisted multiobjective optimization based on decomposition

Author(s) :

Berveglieri, Nicolas [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Derbel, Bilel [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Liefooghe, Arnaud [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Aguirre, Hernan [Auteur]
Faculty of Engineering [Nagano]
Tanaka, Kiyoshi [Auteur]
Faculty of Engineering [Nagano]

Conference title :

GECCO '19 - Proceedings of the Genetic and Evolutionary Computation Conference

City :

Prague

Country :

République tchèque

Start date of the conference :

2019-07-13

Publisher :

ACM Press

English keyword(s) :

benchmarking
Multiobjective optimization
surrogates

HAL domain(s) :

Informatique [cs]/Calcul parallèle, distribué et partagé [cs.DC]
Informatique [cs]/Algorithme et structure de données [cs.DS]
Informatique [cs]/Apprentissage [cs.LG]

English abstract : [en]

A number of surrogate-assisted evolutionary algorithms are being developed for tackling expensive multiobjective optimization problems. On the one hand, a relatively broad range of techniques from both machine learning and ...
Show more >A number of surrogate-assisted evolutionary algorithms are being developed for tackling expensive multiobjective optimization problems. On the one hand, a relatively broad range of techniques from both machine learning and multiobjective optimization can be combined for this purpose. Diferent taxonomies exist in order to better delimit the design choices, advantages and drawbacks of existing approaches. On the other hand, assessing the relative performance of a given approach is a diicult task, since it depends on the characteristics of the problem at hand. In this paper, we focus on surrogate-assisted approaches using objective space decomposition as a core component. We propose a reined and ine-grained classiication, ranging from EGO-like approaches to iltering or pre-screening. More importantly, we provide a comprehensive comparative study of a representative selection of state-of-the-art methods , together with simple baseline algorithms. We rely on selected benchmark functions taken from the bbob-biobj benchmarking test suite, that provides a variable range of objective function diiculties. Our empirical analysis highlights the efect of the available budget on the relative performance of each approach, and the impact of the training set and of the machine learning model construction on both solution quality and runtime eiciency.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

ANR Project :

Optimisation Multiobjectif Big

Collections :

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

Source :

Harvested from HAL