Approximate implementations of pure random search in the presence of noise

David L J Alexander; David W. Bulger; James M. Calvin; H. Edwin Romeijn; Ryan L. Sherriff

doi:10.1007/s10898-004-9970-4

Approximate implementations of pure random search in the presence of noise

David L J Alexander^*, David W. Bulger, James M. Calvin, H. Edwin Romeijn, Ryan L. Sherriff

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

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.

Original language	English
Pages (from-to)	601-612
Number of pages	12
Journal	Journal of Global Optimization
Volume	31
Issue number	4
DOIs	https://doi.org/10.1007/s10898-004-9970-4
Publication status	Published - Apr 2005

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title = "Approximate implementations of pure random search in the presence of noise",

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.",

author = "Alexander, {David L J} and Bulger, {David W.} and Calvin, {James M.} and Romeijn, {H. Edwin} and Sherriff, {Ryan L.}",

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AU - Sherriff, Ryan L.

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AB - 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.

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Approximate implementations of pure random search in the presence of noise

Abstract

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