An adaptive evolution control based on ...
Type de document :
Communication dans un congrès avec actes
Titre :
An adaptive evolution control based on confident regions for surrogate-assisted optimization
Auteur(s) :
Briffoteaux, Guillaume [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Institut de Mathématiques [Mons]
Melab, Nouredine [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Mezmaz, Mohand [Auteur]
Institut de Mathématiques [Mons]
Tuyttens, Daniel [Auteur]
Institut de Mathématiques [Mons]
Optimisation de grande taille et calcul large échelle [BONUS]
Institut de Mathématiques [Mons]
Melab, Nouredine [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Mezmaz, Mohand [Auteur]
Institut de Mathématiques [Mons]
Tuyttens, Daniel [Auteur]
Institut de Mathématiques [Mons]
Titre de la manifestation scientifique :
HPCS 2018 - International Conference on High Performance Computing & Simulation
Ville :
Orléans
Pays :
France
Date de début de la manifestation scientifique :
2018-07-16
Mot(s)-clé(s) en anglais :
machine learning
evolution control
Surrogate-modeling
multi-objective optimization
direct fitness replacement
evolution control
Surrogate-modeling
multi-objective optimization
direct fitness replacement
Discipline(s) HAL :
Mathématiques [math]/Optimisation et contrôle [math.OC]
Résumé en anglais : [en]
In simulation-based optimization the objective function is often computationally expensive for many optimization problems. Surrogate-assisted optimization is therefore a major approach to efficiently solve them. One of the ...
Lire la suite >In simulation-based optimization the objective function is often computationally expensive for many optimization problems. Surrogate-assisted optimization is therefore a major approach to efficiently solve them. One of the major issues of this approach is how to integrate the approximate models (surrogates or metamodels) in the optimization process. The challenge is to find the best trade-off between the quality (in terms of precision) of the provided solutions and the efficiency (in terms of execution time) of the resolution. In this paper, we investigate the evolution control that alternates between the simulator and the surrogate within the optimization process. We propose an adaptive evolution control mechanism based on the distance-based concept of confident regions. The approach has been integrated into an ANN-assisted NSGA-2 and experimented using the ZDT4 multi-modal benchmark function. The reported results show that the proposed approach outperforms two other existing ones.Lire moins >
Lire la suite >In simulation-based optimization the objective function is often computationally expensive for many optimization problems. Surrogate-assisted optimization is therefore a major approach to efficiently solve them. One of the major issues of this approach is how to integrate the approximate models (surrogates or metamodels) in the optimization process. The challenge is to find the best trade-off between the quality (in terms of precision) of the provided solutions and the efficiency (in terms of execution time) of the resolution. In this paper, we investigate the evolution control that alternates between the simulator and the surrogate within the optimization process. We propose an adaptive evolution control mechanism based on the distance-based concept of confident regions. The approach has been integrated into an ANN-assisted NSGA-2 and experimented using the ZDT4 multi-modal benchmark function. The reported results show that the proposed approach outperforms two other existing ones.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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