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Résumé
Cooperative co-evolutionary algorithms, especially those able to uncover interaction structure between variables, have a great potential in optimizing large-scale problems. Nevertheless, they are expensive in terms of number of function evaluations and this issue can be quite problematic when dealing with computationally expensive optimization problems. An effective approach to deal with such problems lies in the exploitation of surrogate models. The latter ones work as cheap-to-evaluate alternatives to the expensive function reducing the computational cost, while still providing improved designs. This process, called surrogate-assisted optimization, is very effective on small-dimensional problems but is not suitable to solve large-scale problems due to the curse of dimensionality. In this paper, a new algorithm, taking benefit from cooperative coevolution and surrogate models, is introduced to efficiently solve high-dimensional, expensive and black-box problems. The proposed algorithm uses recursive differential grouping to perform an accurate problem decomposition. Experimental results are provided on a set of 1000-dimensional problems and show promising results.
langue originale | Anglais |
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titre | 2019 IEEE Congress on Evolutionary Computation (CEC) |
Editeur | IEEE |
Pages | 674-681 |
Nombre de pages | 8 |
ISBN (Electronique) | 978-1-7281-2153-6 |
ISBN (imprimé) | 978-1-7281-2154-3 |
Les DOIs | |
Etat de la publication | Publié - 8 août 2019 |
Evénement | 2019 IEEE Congress On Evolutionary Computation - Te Papa Tongarewa, Wellington, Nouvelle-Zélande Durée: 10 juin 2019 → 13 juin 2019 http://cec2019.org/ |
Série de publications
Nom | 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings |
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Une conférence
Une conférence | 2019 IEEE Congress On Evolutionary Computation |
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Titre abrégé | CEC 2019 |
Pays/Territoire | Nouvelle-Zélande |
La ville | Wellington |
période | 10/06/19 → 13/06/19 |
Adresse Internet |
mots-clés
- global optimization
- surrogate-assisted optimization
- Large-scale optimization
- high dimensional , expensive and black-box problems
- cooperative co-evolutionary algorithm
- differential grouping
- evolutionary algorithm
Empreinte digitale
Examiner les sujets de recherche de « A Surrogate-Assisted Cooperative Co-evolutionary Algorithm Using Recursive Differential Grouping as Decomposition Strategy ». Ensemble, ils forment une empreinte digitale unique.Projets
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CÉCI – Consortium des Équipements de Calcul Intensif
Champagne, B., Lazzaroni, R., Geuzaine , C., Chatelain, P. & Knaepen, B.
1/01/18 → 31/12/22
Projet: Recherche
Équipement
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Plateforme Technologique Calcul Intensif
Benoît Champagne (!!Manager)
Plateforme technologique Calcul intensifEquipement/installations: Plateforme technolgique
Thèses de l'étudiant
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Challenging High Dimensionality in Evolutionary Optimization using Cooperative Co-evolutionary Algorithms
Auteur: Blanchard, J., 29 juin 2021Superviseur: Carletti, T. (Promoteur), SARTENAER, A. (Jury), BEAUTHIER, C. (Jury), Mayer, A. (Jury), Tuyttens, D. (Personne externe) (Jury) & El-Abd, M. (Personne externe) (Jury)
Student thesis: Doc types › Docteur en Sciences
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