Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Paul Feliot"'
Publikováno v:
Advanced Modeling and Simulation in Engineering Sciences, Vol 7, Iss 1, Pp 1-20 (2020)
Abstract This article addresses the problem of constrained derivative-free optimization in a multi-fidelity (or variable-complexity) framework using Bayesian optimization techniques. It is assumed that the objective and constraints involved in the op
Externí odkaz:
https://doaj.org/article/2ea95548677747259391da2d27f9b9be
Publikováno v:
Structural and Multidisciplinary Optimization. 65
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2a779f776202dc641059485e7e1643ac
https://doi.org/10.1007/s00158-021-03090-z
https://doi.org/10.1007/s00158-021-03090-z
Publikováno v:
Advanced Modeling and Simulation in Engineering Sciences
Advanced Modeling and Simulation in Engineering Sciences, SpringerOpen, 2020, 7 (1), ⟨10.1186/s40323-020-00176-z⟩
Advanced Modeling and Simulation in Engineering Sciences, Vol 7, Iss 1, Pp 1-20 (2020)
Advanced Modeling and Simulation in Engineering Sciences, SpringerOpen, 2020, 7 (1), ⟨10.1186/s40323-020-00176-z⟩
Advanced Modeling and Simulation in Engineering Sciences, Vol 7, Iss 1, Pp 1-20 (2020)
This article addresses the problem of constrained derivative-free optimization in a multi-fidelity (or variable-complexity) framework using Bayesian optimization techniques. It is assumed that the objective and constraints involved in the optimizatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f86347e28d3400371c9878ba54b49c6d
https://doi.org/10.1186/s40323-020-00176-z
https://doi.org/10.1186/s40323-020-00176-z
Publikováno v:
Machine Learning, Optimization, and Data Science ISBN: 9783030137083
LOD
Machine Learning, Optimization, and Data Science. 4th International Conference (LOD 2018)
Machine Learning, Optimization, and Data Science. 4th International Conference (LOD 2018), Sep 2018, Volterra, Italy. pp.533-544
LOD
Machine Learning, Optimization, and Data Science. 4th International Conference (LOD 2018)
Machine Learning, Optimization, and Data Science. 4th International Conference (LOD 2018), Sep 2018, Volterra, Italy. pp.533-544
In this article, we present a framework for taking into account user preferences in multi-objective Bayesian optimization in the case where the objectives are expensive-to-evaluate black-box functions. A novel expected improvement criterion to be use
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::00068b372c4c41e170e194eec40fffd2
https://doi.org/10.1007/978-3-030-13709-0_45
https://doi.org/10.1007/978-3-030-13709-0_45
Publikováno v:
Journées annuelles du GdR MASCOT NUM (MASCOT NUM 2015)
Journées annuelles du GdR MASCOT NUM (MASCOT NUM 2015), Apr 2015, Saint-Etienne, France. 2015
Lecture Notes in Computer Science ISBN: 9783319190839
LION
Learning and Intelligent Optimization: 9th International Conference, LION 9. Revised Selected Papers.
Learning and Intelligent Optimization: 9th International Conference, LION 9. Revised Selected Papers., Jan 2015, Lille, France. pp.256-261, ⟨10.1007/978-3-319-19084-6_24⟩
Journées annuelles du GdR MASCOT NUM (MASCOT NUM 2015), Apr 2015, Saint-Etienne, France. 2015
Lecture Notes in Computer Science ISBN: 9783319190839
LION
Learning and Intelligent Optimization: 9th International Conference, LION 9. Revised Selected Papers.
Learning and Intelligent Optimization: 9th International Conference, LION 9. Revised Selected Papers., Jan 2015, Lille, France. pp.256-261, ⟨10.1007/978-3-319-19084-6_24⟩
National audience; This communication addresses the problem of derivative-free multi-objective optimization of real-valued functions subject to multiple inequality constraints, under a Bayesian framework. Both the objective and constraint functions a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9cf0a87da3f46b042b3e8aee7b110a98
https://hal-centralesupelec.archives-ouvertes.fr/hal-01323031/file/Poster_PF_MascotNum15.pdf
https://hal-centralesupelec.archives-ouvertes.fr/hal-01323031/file/Poster_PF_MascotNum15.pdf