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http://hdl.handle.net/1942/42252
Title: | A Bayesian Optimization Algorithm for Constrained Simulation Optimization Problems with Heteroscedastic Noise | Authors: | AMINI, Sasan VAN NIEUWENHUYSE, Inneke |
Issue Date: | 2023 | Publisher: | Springer | Source: | Sellmann, Meinolf; Tierney, Kevin (Ed.). Learning and Intelligent Optimization 17th International Conference, LION 17, Nice, France, June 4–8, 2023, Revised Selected Papers, Springer, p. 78 -91 | Series/Report: | Lecture Notes in Computer Science | Series/Report no.: | 14286 | Abstract: | In this research, we develop a Bayesian optimization algorithm to solve expensive, constrained problems. We consider the presence of heteroscedastic noise in the evaluations and thus propose a new acquisition function to account for this noise in the search for the optimal point. We use stochastic kriging to fit the metamodels, and we provide computational results to highlight the importance of accounting for the heteroscedastic noise in the search for the optimal solution. Finally, we propose some promising directions for further research. | Keywords: | Bayesian optimization;Constrained problems;Heteroscedastic noise;Stochastic Kriging;Barrier function | Document URI: | http://hdl.handle.net/1942/42252 | ISBN: | 9783031445040 9783031445057 |
ISSN: | 0302-9743 | DOI: | 10.1007/978-3-031-44505-7_6 | Category: | C1 | Type: | Proceedings Paper |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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8144_20240126172552.pdf Restricted Access | Published version | 765.31 kB | Adobe PDF | View/Open Request a copy |
LION_22_Constrained (1).pdf Until 2024-10-23 | Peer-reviewed author version | 1.44 MB | Adobe PDF | View/Open Request a copy |
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