Approximate implementations of pure random search in the presence of noise

Authors

David L.J. Alexander, Massey University Wellington
David W. Bulger, Macquarie University
James M. Calvin, New Jersey Institute of Technology
H. Edwin Romeijn, Herbert Wertheim College of Engineering
Ryan L. Sherriff, Massey University

Document Type

Conference Proceeding

Publication Date

4-1-2005

Abstract

We discuss the noisy optimisation problem, in which function evaluations are subject to random noise. Adaptation of pure random search to noisy optimisation by repeated sampling is considered. We introduce and exploit an improving bias condition on noise-affected pure random search algorithms. Two such algorithms are considered; we show that one requires infinite expected work to proceed, while the other is practical. © Springer 2005.

Identifier

22044433804 (Scopus)

Publication Title

Journal of Global Optimization

External Full Text Location

https://doi.org/10.1007/s10898-004-9970-4

ISSN

09255001

First Page

601

Last Page

612

Issue

4

Volume

31

Fund Ref

Marsden Fund

Recommended Citation

Alexander, David L.J.; Bulger, David W.; Calvin, James M.; Romeijn, H. Edwin; and Sherriff, Ryan L., "Approximate implementations of pure random search in the presence of noise" (2005). Faculty Publications. 19738.
https://digitalcommons.njit.edu/fac_pubs/19738