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Cascade Bayesian optimization

Nguyen, Thanh Dai, Gupta, Sunil, Rana, Santu, Nguyen, Vu, Venkatesh, Svetha, Deane, Kyle J. and Sanders, Paul G. 2016, Cascade Bayesian optimization, in AI 2016: Advances in Artificial Intelligence : Proceedings of the Australasian Joint Conference, Springer International Publishing, Cham, Switzerland, pp. 268-280, doi: 10.1007/978-3-319-50127-7_22.

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Title Cascade Bayesian optimization
Author(s) Nguyen, Thanh Dai
Gupta, SunilORCID iD for Gupta, Sunil orcid.org/0000-0002-3308-1930
Rana, SantuORCID iD for Rana, Santu orcid.org/0000-0003-2247-850X
Nguyen, Vu
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Deane, Kyle J.
Sanders, Paul G.
Conference name Advances in Artificial Intelligence. Australasian Joint Conference (29th : 2016 : Hobart, Tasmania)
Conference location Hobart, Tasmania
Conference dates 5-8 Dec. 2016
Title of proceedings AI 2016: Advances in Artificial Intelligence : Proceedings of the Australasian Joint Conference
Editor(s) Kang, Byeong Ho
Bai, Quan
Publication date 2016
Series Lecture notes in artificial intelligence
Conference series Advances in Artificial Intelligence Australasian Joint Conference
Start page 268
End page 280
Total pages 13
Publisher Springer International Publishing
Place of publication Cham, Switzerland
Summary Multi-stage cascade processes are fairly common, especially in manufacturing industry. Precursors or raw materials are transformed at each stage before being used as the input to the next stage. Setting the right control parameters at each stage is important to achieve high quality products at low cost. Finding the right parameters via trial and error approach can be time consuming. Bayesian optimization is an efficient way to optimize costly black-box function. We extend the standard Bayesian optimization approach to the cascade process through formulating a series of optimization problems that are solved sequentially from the final stage to the first stage. Epistemic uncertainties are effectively utilized in the formulation. Further, cost of the parameters are also included to find cost-efficient solutions. Experiments performed on a simulated testbed of Al-Sc heat treatment through a three-stage process showed considerable efficiency gain over a naïve optimization approach.
ISBN 9783319501260
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-319-50127-7_22
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 0 Not Applicable
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2016, Springer International
Persistent URL http://hdl.handle.net/10536/DRO/DU:30090641

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