Selecting an Optimal Subset with Regression Metamodels

Fei Gao; Yanwen Li; Siyang Gao; Hui Xiao

doi:10.1109/CCTA.2019.8920514

Selecting an Optimal Subset with Regression Metamodels

Fei Gao, Yanwen Li^*, Siyang Gao, Hui Xiao

^*Corresponding author for this work

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

2 Citations (Scopus)

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.

Original language	English
Title of host publication	IEEE CCTA 2019
Subtitle of host publication	3^rd IEEE Conference on Control Technology and Applications
Publisher	IEEE
Pages	851-855
ISBN (Electronic)	978-1-7281-2767-5
ISBN (Print)	978-1-7281-2768-2
DOIs	https://doi.org/10.1109/CCTA.2019.8920514
Publication status	Published - Aug 2019
Event	3rd IEEE Conference on Control Technology and Applications, IEEE CCTA 2019 - City University of Hong Kong, Hong Kong, China Duration: 19 Aug 2019 → 21 Aug 2019 http://ccta2019.ieeecss.org/

Publication series

Name	CCTA - IEEE Conference on Control Technology and Applications

Conference

Conference	3rd IEEE Conference on Control Technology and Applications, IEEE CCTA 2019
Place	Hong Kong, China
Period	19/08/19 → 21/08/19
Internet address	http://ccta2019.ieeecss.org/

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10.1109/CCTA.2019.8920514

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@inproceedings{c2ed47fe61a24973b7039deeb3dbf246,

title = "Selecting an Optimal Subset with Regression Metamodels",

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.",

author = "Fei Gao and Yanwen Li and Siyang Gao and Hui Xiao",

year = "2019",

month = aug,

doi = "10.1109/CCTA.2019.8920514",

language = "English",

isbn = "978-1-7281-2768-2",

series = "CCTA - IEEE Conference on Control Technology and Applications",

publisher = "IEEE",

pages = "851--855",

booktitle = "IEEE CCTA 2019",

address = "United States",

note = "3rd IEEE Conference on Control Technology and Applications, IEEE CCTA 2019 ; Conference date: 19-08-2019 Through 21-08-2019",

url = "http://ccta2019.ieeecss.org/",

}

Gao, F , Li, Y , Gao, S & Xiao, H 2019, Selecting an Optimal Subset with Regression Metamodels. in IEEE CCTA 2019: 3^rd IEEE Conference on Control Technology and Applications., 8920514, CCTA - IEEE Conference on Control Technology and Applications, IEEE, pp. 851-855, 3rd IEEE Conference on Control Technology and Applications, IEEE CCTA 2019, Hong Kong, China, 19/08/19. https://doi.org/10.1109/CCTA.2019.8920514

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

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

TY - GEN

T1 - Selecting an Optimal Subset with Regression Metamodels

AU - Gao, Fei

AU - Li, Yanwen

AU - Gao, Siyang

AU - Xiao, Hui

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Y1 - 2019/8

N2 - 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.

AB - 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.

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U2 - 10.1109/CCTA.2019.8920514

DO - 10.1109/CCTA.2019.8920514

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-1-7281-2768-2

T3 - CCTA - IEEE Conference on Control Technology and Applications

SP - 851

EP - 855

BT - IEEE CCTA 2019

PB - IEEE

T2 - 3rd IEEE Conference on Control Technology and Applications, IEEE CCTA 2019

Y2 - 19 August 2019 through 21 August 2019

ER -

Selecting an Optimal Subset with Regression Metamodels

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