Optimization via simulation using Gaussian process-based search

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review

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Author(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings - Winter Simulation Conference
Pages4134-4145
StatePublished - 2011
Externally publishedYes

Publication series

Name
ISSN (Print)0891-7736

Conference

Title2011 Winter Simulation Conference, WSC 2011
PlaceUnited States
CityPhoenix, AZ
Period11 - 14 December 2011

Abstract

Random search algorithms are often used to solve optimization-via- simulation (OvS) problems. The most critical component of a random search algorithm is the sampling distribution that is used to guide the allocation of the search effort. A good sampling distribution can balance the tradeoff between the effort used in searching around the current best solution (which is called exploitation) and the effort used in searching largely unknown regions (which is called exploration). However, most of the random search algorithms for OvS problems have difficulties in balancing this tradeoff in a seamless way. In this paper we propose a new random search algorithm, called the Gaussian Process-based Search (GPS) algorithm, which derives a sampling distribution from a fast fitted Gaussian process in each iteration of the algorithm. We show that the sampling distribution has the desired properties and it can automatically balance the exploitation and exploration tradeoff. © 2011 IEEE.

Citation Format(s)

Optimization via simulation using Gaussian process-based search. / Sun, Lihua; Hong, L. Jeff; Hu, Zhaolin.

Proceedings - Winter Simulation Conference. 2011. p. 4134-4145 6148102.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review