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BMOO: a Bayesian Multi-Objective Optimization algorithm

Paul Feliot 1, 2, 3 Julien Bect 1, 2, 3 Emmanuel Vazquez 1, 2, 3 
1 GdR MASCOT-NUM - Méthodes d'Analyse Stochastique des Codes et Traitements Numériques
2 L2S - Laboratoire des signaux et systèmes
3 IRT SystemX - IRT SystemX
Abstract : We address the problem of derivative-free multi-objective optimization of real-valued functions subject to multiple inequality constraints.
Keywords : kriging Subset simulation multi-objective optimization Bayesian optimization Gaussian processes Expected improvement Sequential Monte-Carlo
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Submitted on : Friday, October 7, 2016 - 3:20:13 PM
Last modification on : Sunday, June 26, 2022 - 2:26:29 AM
Long-term archiving on: : Friday, February 3, 2017 - 7:13:57 PM

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  • HAL Id : hal-01377761, version 1

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CNRS | SUP_LSS | INSMI | SUP_SIGNAUX | CENTRALESUPELEC | TDS-MACS | IRT-SYSTEMX | UNIV-PARIS-SACLAY | GS-ENGINEERING | GS-COMPUTER-SCIENCE

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Paul Feliot, Julien Bect, Emmanuel Vazquez. BMOO: a Bayesian Multi-Objective Optimization algorithm. Journées annuelles du GdR MASCOT NUM (MASCOT NUM 2016), Mar 2016, Toulouse, France. ⟨hal-01377761⟩

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