Selecting an Optimal Subset with Regression Metamodels

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

1 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Title of host publicationIEEE CCTA 2019
Subtitle of host publication3rd IEEE Conference on Control Technology and Applications
PublisherInstitute of Electrical and Electronics Engineers
Pages851-855
ISBN (Electronic)978-1-7281-2767-5
ISBN (Print)978-1-7281-2768-2
Publication statusPublished - Aug 2019

Publication series

NameCCTA - IEEE Conference on Control Technology and Applications

Conference

Title3rd IEEE Conference on Control Technology and Applications, IEEE CCTA 2019
LocationCity University of Hong Kong
PlaceHong Kong
Period19 - 21 August 2019

Abstract

In this paper, we consider the ranking and selection problem of selecting the optimal subset from a finite set of alternative designs. Given the total simulation budget constraint, we aim to maximize the probability of correctly selecting the top-m designs. In order to further improve the selection efficiency, we incorporate the information from across the domain into quadratic regression equation. Under some common assumptions in most regression based approaches, we propose an approximately optimal rule that determines the design locations need to be simulated and the number of simulation replications allocated to the selected designs. Numerical experiments demonstrate that our approach dramatically improves the selection efficiency on some typical selection examples compared to the existing approaches.

Citation Format(s)

Selecting an Optimal Subset with Regression Metamodels. / Gao, Fei; Li, Yanwen; Gao, Siyang et al.
IEEE CCTA 2019: 3rd IEEE Conference on Control Technology and Applications. Institute of Electrical and Electronics Engineers, 2019. p. 851-855 8920514 (CCTA - IEEE Conference on Control Technology and Applications).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review