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

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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 languageEnglish (US)
Pages (from-to)601-612
Number of pages12
JournalJournal of Global Optimization
Volume31
Issue number4
DOIs
StatePublished - Apr 2005

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Management Science and Operations Research
  • Control and Optimization
  • Applied Mathematics

Keywords

  • Global optimisation
  • Noisy objective function
  • Pure random search
  • Sequential analysis

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