Surrogate-assisted Multi-objective ...
Type de document :
Communication dans un congrès avec actes
DOI :
Titre :
Surrogate-assisted Multi-objective Combinatorial Optimization based on Decomposition and Walsh Basis
Auteur(s) :
Pruvost, Geoffrey [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]
Japanese French Laboratory for Informatics [JFLI]
Optimisation de grande taille et calcul large échelle [BONUS]
Verel, Sébastien [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Zhang, Qingfu [Auteur]
City University of Hong Kong [Hong Kong] [CUHK]
Optimisation de grande taille et calcul large échelle [BONUS]
Derbel, Bilel [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Liefooghe, Arnaud [Auteur]
Japanese French Laboratory for Informatics [JFLI]
Optimisation de grande taille et calcul large échelle [BONUS]
Verel, Sébastien [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Zhang, Qingfu [Auteur]
City University of Hong Kong [Hong Kong] [CUHK]
Titre de la manifestation scientifique :
GECCO '20 - Genetic and Evolutionary Computation Conference
Ville :
Cancun
Pays :
Mexique
Date de début de la manifestation scientifique :
2020-07-08
Titre de l’ouvrage :
GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference
Éditeur :
Association for Computing Machinery (ACM)
Date de publication :
2020-06-26
Discipline(s) HAL :
Informatique [cs]
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Intelligence artificielle [cs.AI]
Résumé en anglais : [en]
We consider the design and analysis of surrogate-assisted algorithms for expensive multi-objective combinatorial optimization. Focusing on pseudo-boolean functions, we leverage existing techniques based on Walsh basis to ...
Lire la suite >We consider the design and analysis of surrogate-assisted algorithms for expensive multi-objective combinatorial optimization. Focusing on pseudo-boolean functions, we leverage existing techniques based on Walsh basis to operate under the decomposition framework of MOEA/D. We investigate two design components for the cheap generation of a promising pool of offspring and the actual selection of one solution for expensive evaluation. We propose different variants, ranging from a filtering approach that selects the most promising solution at each iteration by using the constructed Walsh surrogates to discriminate between a pool of offspring generated by variation, to a substitution approach that selects a solution to evaluate by optimizing the Walsh surrogates in a multi-objective manner. Considering bi-objective NK landscapes as benchmark problems offering different degree of non-linearity, we conduct a comprehensive empirical analysis including the properties of the achievable approximation sets, the anytime performance, and the impact of the order used to train the Walsh surrogates. Our empirical findings show that, although our surrogate-assisted design is effective, the optimal integration of Walsh models within a multi-objective evolutionary search process gives rise to particular questions for which different trade-off answers can be obtained.Lire moins >
Lire la suite >We consider the design and analysis of surrogate-assisted algorithms for expensive multi-objective combinatorial optimization. Focusing on pseudo-boolean functions, we leverage existing techniques based on Walsh basis to operate under the decomposition framework of MOEA/D. We investigate two design components for the cheap generation of a promising pool of offspring and the actual selection of one solution for expensive evaluation. We propose different variants, ranging from a filtering approach that selects the most promising solution at each iteration by using the constructed Walsh surrogates to discriminate between a pool of offspring generated by variation, to a substitution approach that selects a solution to evaluate by optimizing the Walsh surrogates in a multi-objective manner. Considering bi-objective NK landscapes as benchmark problems offering different degree of non-linearity, we conduct a comprehensive empirical analysis including the properties of the achievable approximation sets, the anytime performance, and the impact of the order used to train the Walsh surrogates. Our empirical findings show that, although our surrogate-assisted design is effective, the optimal integration of Walsh models within a multi-objective evolutionary search process gives rise to particular questions for which different trade-off answers can be obtained.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Projet ANR :
Collections :
Source :
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