Learning variable importance to guide ...
Type de document :
Communication dans un congrès avec actes
Titre :
Learning variable importance to guide recombination
Auteur(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]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Derbel, Bilel [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Verel, Sébastien [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Tanaka, Kiyoshi [Auteur]
Faculty of Engineering [Nagano]
Faculty of Engineering [Nagano]
Aguirre, Hernan [Auteur]
Faculty of Engineering [Nagano]
Daolio, Fabio [Auteur]
Faculty of Engineering [Nagano]
Liefooghe, Arnaud [Auteur]
![refId](/themes/Mirage2//images/idref.png)
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Derbel, Bilel [Auteur]
![refId](/themes/Mirage2//images/idref.png)
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Verel, Sébastien [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Tanaka, Kiyoshi [Auteur]
Faculty of Engineering [Nagano]
Titre de la manifestation scientifique :
IEEE Symposium on Computational Intelligence
Ville :
Athens
Pays :
Grèce
Date de début de la manifestation scientifique :
2016-12-06
Titre de l’ouvrage :
IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making (IEEE MCDM 2016)
Date de publication :
2016
Discipline(s) HAL :
Computer Science [cs]/Operations Research [math.OC]
Mathématiques [math]/Optimisation et contrôle [math.OC]
Mathématiques [math]/Optimisation et contrôle [math.OC]
Résumé en anglais : [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, ...
Lire la suite >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.Lire moins >
Lire la suite >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.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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