research-article

A novel sequential design strategy for global surrogate modeling

Authors:

Karel Crombecq,

Luciano De Tommasi,

Tom DhaeneAuthors Info & Claims

WSC '09: Winter Simulation Conference

Pages 731 - 742

Published: 13 December 2009 Publication History

Abstract

In mathematical/statistical modeling of complex systems, the locations of the data points are essential to the success of the algorithm. Sequential design methods are iterative algorithms that use data acquired from previous iterations to guide future sample selection. They are often used to improve an initial design such as a Latin hypercube or a simple grid, in order to focus on highly dynamic parts of the design space. In this paper, a comparison is made between different sequential design methods for global surrogate modeling on a real-world electronics problem. Existing exploitation and exploration-based methods are compared against a novel hybrid technique which incorporates both an exploitation criterion, using local linear approximations of the objective function, and an exploration criterion, using a Monte Carlo Voronoi tessellation. The test results indicate that a considerable improvement of the average model accuracy can be achieved by using this new approach.

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Cited By

Lei YDong WZhu ZSarjoughian H(2019)A sequential neighbor exploratory experimental design method for complex simulation metamodelingProceedings of the Theory of Modeling and Simulation Symposium10.5555/3338246.3338263(1-10)Online publication date: 29-Apr-2019
https://dl.acm.org/doi/10.5555/3338246.3338263
Singh PHellander A(2017)Surrogate assisted model reduction for stochastic biochemical reaction networksProceedings of the 2017 Winter Simulation Conference10.5555/3242181.3242328(1-11)Online publication date: 3-Dec-2017
https://dl.acm.org/doi/10.5555/3242181.3242328
Kianifar MCampean FWood A(2016)Application of permutation genetic algorithm for sequential model building---model validation design of experimentsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-015-1929-520:8(3023-3044)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1007/s00500-015-1929-5
Show More Cited By

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Information & Contributors

Information

Published In

cover image ACM Conferences

WSC '09: Winter Simulation Conference

December 2009

3211 pages

ISBN:9781424457717

General Chair:
Ann Dunkin
Palo Alto Unified School District
,
Program Chair:
Ricki G. Ingalls
Oklahoma State University

Sponsors

IIE: Institute of Industrial Engineers
INFORMS-SIM: Institute for Operations Research and the Management Sciences: Simulation Society
SIGSIM: ACM Special Interest Group on Simulation and Modeling
SCS: Society for Modeling and Simulation International

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Winter Simulation Conference

Publication History

Published: 13 December 2009

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WSC09

Sponsor:

IIE
INFORMS-SIM
SIGSIM
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WSC09: Winter Simulation Conference

December 13 - 16, 2009

Texas, Austin

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WSC '09 Paper Acceptance Rate 137 of 256 submissions, 54%;

Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

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Cited By

Lei YDong WZhu ZSarjoughian H(2019)A sequential neighbor exploratory experimental design method for complex simulation metamodelingProceedings of the Theory of Modeling and Simulation Symposium10.5555/3338246.3338263(1-10)Online publication date: 29-Apr-2019
https://dl.acm.org/doi/10.5555/3338246.3338263
Singh PHellander A(2017)Surrogate assisted model reduction for stochastic biochemical reaction networksProceedings of the 2017 Winter Simulation Conference10.5555/3242181.3242328(1-11)Online publication date: 3-Dec-2017
https://dl.acm.org/doi/10.5555/3242181.3242328
Kianifar MCampean FWood A(2016)Application of permutation genetic algorithm for sequential model building---model validation design of experimentsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-015-1929-520:8(3023-3044)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1007/s00500-015-1929-5
Kahng ALin BNath SMacii E(2013)Enhanced metamodeling techniques for high-dimensional IC design estimation problemsProceedings of the Conference on Design, Automation and Test in Europe10.5555/2485288.2485726(1861-1866)Online publication date: 18-Mar-2013
https://dl.acm.org/doi/10.5555/2485288.2485726
Nguyen HCouckuyt IKnockaert LDhaene TGorissen DSaeys Y(2011)An alternative approach to avoid overfitting for surrogate modelsProceedings of the Winter Simulation Conference10.5555/2431518.2431848(2765-2776)Online publication date: 11-Dec-2011
https://dl.acm.org/doi/10.5555/2431518.2431848
Groot PBirlutiu AHeskes T(2010)Bayesian Monte Carlo for the Global Optimization of Expensive FunctionsProceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence10.5555/1860967.1861017(249-254)Online publication date: 4-Aug-2010
https://dl.acm.org/doi/10.5555/1860967.1861017
Gorissen DDhaene TTurck F(2009)Evolutionary Model Type Selection for Global Surrogate ModelingThe Journal of Machine Learning Research10.5555/1577069.175585410(2039-2078)Online publication date: 1-Dec-2009
https://dl.acm.org/doi/10.5555/1577069.1755854

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