Kriging-based simulation-optimization: A stochastic recursion perspective

Giulia Pedrielli, Szu Hui Ng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Motivated by our recent extension of the Two-Stage Sequential Algorithm (eTSSO), we propose an adaptation of the framework in Pasupathy et al. (2015) for the study of convergence of kriging-based procedures. Specifically, we extend the proof scheme in Pasupathy et al. (2015) to the class of kriging-based simulation-optimization algorithms. In particular, the asymptotic convergence and the convergence rate of eTSSO are investigated by interpreting the kriging-based search as a stochastic recursion. We show the parallelism between the two paradigms and exploit the deterministic counterpart of eTSSO, the more famous Efficient Global Optimization (EGO) procedure, in order to derive eTSSO structural properties. This work represents a first step towards a general proof framework for the asymptotic convergence and convergence rate analysis of meta-model based simulation-optimization.

Original languageEnglish (US)
Title of host publication2015 Winter Simulation Conference, WSC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3834-3845
Number of pages12
ISBN (Electronic)9781467397438
DOIs
StatePublished - Feb 16 2016
Externally publishedYes
EventWinter Simulation Conference, WSC 2015 - Huntington Beach, United States
Duration: Dec 6 2015Dec 9 2015

Publication series

NameProceedings - Winter Simulation Conference
Volume2016-February
ISSN (Print)0891-7736

Other

OtherWinter Simulation Conference, WSC 2015
CountryUnited States
CityHuntington Beach
Period12/6/1512/9/15

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Kriging-based simulation-optimization: A stochastic recursion perspective'. Together they form a unique fingerprint.

Cite this