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 journalArticle

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

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