Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.14/8394
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- Title
- Approximate implementations of pure random search in the presence of noise
- Related
- Journal of global optimization, Vol. 31, Issue 4, p.601-612
- DOI
- 10.1007/s10898-004-9970-4
- Publisher
- Springer
- Date
- 2005
- Author/Creator
- Alexander, David L. J
- Author/Creator
- Bulger, David W
- Author/Creator
- Calvin, James M
- Author/Creator
- Romeijn, H. Edwin
- Author/Creator
- Sherriff, Ryan L
- Description
- 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.
- Description
- 12 page(s)
- Subject Keyword
- global optimisation
- Subject Keyword
- noisy objective function
- Subject Keyword
- pure random search
- Subject Keyword
- sequential analysis
- Resource Type
- journal article
- Organisation
- Macquarie University. Dept. of Statistics
- Identifier
- http://hdl.handle.net/1959.14/8394
- Identifier
- ISSN:1573-2916
- Identifier
- mq-rm-2005002486
- Language
- eng
- Reviewed
