Gradients of Acquisition Functions for Bi-objective Bayesian optimization

Kaifeng Yang, Sixuan Liu, Michael Affenzeller, Guozhi Dong

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

Abstract

Multi-objective Bayesian optimization (MOBO) searches for optimal solutions by maximizing acquisition functions to optimize expensive black-box functions globally. MOBO frequently employs the probability of improvement (POI), expected hypervolume improvement (EHVI), and truncated expected hypervolume improvement (TEHVI) acquisition functions. Based on the POI, this paper proposes the truncated probability of improvement (TPoI) that leverages prior knowledge of objective values via the truncated normal distribution. Additionally, this paper proposes explicit formulas for computing the gradient of POI, the gradient of TPoI, and the gradient of TEHVI.

OriginalspracheEnglisch
TitelICNC-FSKD 2023 - 2023 19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
Redakteure/-innenLiang Zhao, Guanglu Sun, Kenli Li, Zheng Xiao, Lipo Wang
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
ISBN (elektronisch)9798350304398
DOIs
PublikationsstatusVeröffentlicht - 2023
Veranstaltung19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2023 - Harbin, China
Dauer: 29 Juli 202331 Juli 2023

Publikationsreihe

NameICNC-FSKD 2023 - 2023 19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery

Konferenz

Konferenz19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2023
Land/GebietChina
OrtHarbin
Zeitraum29.07.202331.07.2023

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