Title :

Learning variable importance to guide recombination

Author(s) :

Sagawa, Miyako [Auteur]
Faculty of Engineering [Nagano]
Aguirre, Hernan [Auteur]
Faculty of Engineering [Nagano]
Daolio, Fabio [Auteur]
Faculty of Engineering [Nagano]
Liefooghe, Arnaud [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Derbel, Bilel [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Verel, Sébastien [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Tanaka, Kiyoshi [Auteur]
Faculty of Engineering [Nagano]

Conference title :

IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making (IEEE MCDM 2016)

City :

Athens

Country :

Grèce

Start date of the conference :

2016

Book title :

IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making (IEEE MCDM 2016)

Publication date :

2016

HAL domain(s) :

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

English abstract : [en]

In evolutionary multi-objective optimization, variation operators are crucially important to produce improving solutions, hence leading the search towards the most promising regions of the solution space. In this paper, ...
Show more >In evolutionary multi-objective optimization, variation operators are crucially important to produce improving solutions, hence leading the search towards the most promising regions of the solution space. In this paper, we propose to use a machine learning modeling technique, namely random forest, in order to estimate, at each iteration in the course of the search process, the importance of decision variables with respect to convergence to the Pareto front. Accordingly, we are able to propose an adaptive mechanism guiding the recombination step with the aim of stressing the convergence of the so-obtained offspring. By conducting an experimental analysis using some of the WFG and DTLZ benchmark test problems, we are able to elicit the behavior of the proposed approach, and to demonstrate the benefits of incorporating machine learning techniques in order to design new efficient adaptive variation mechanisms.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