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Stopping rules in k-adaptive global random search algorithms

Zhigljavsky, Anatoly Alexandrovich and Hamilton, Emily 2010. Stopping rules in k-adaptive global random search algorithms. Journal of Global Optimization 48 (1) , pp. 87-97. 10.1007/s10898-010-9528-6

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Abstract

In this paper we develop a methodology for defining stopping rules in a general class of global random search algorithms that are based on the use of statistical procedures. To build these stopping rules we reach a compromise between the expected increase in precision of the statistical procedures and the expected waiting time for this increase in precision to occur.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Mathematics
Subjects: Q Science > QA Mathematics
Publisher: Springer
ISSN: 1573-2916
Last Modified: 04 Jun 2017 03:00
URI: http://orca.cf.ac.uk/id/eprint/15205

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Cited 2 times in Google Scholar. View in Google Scholar

Cited 4 times in Scopus. View in Scopus. Powered By Scopus® Data

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