Matching Stochastic Algorithms to Objective Function Landscapes

W.P. Baritompa, M. Dur, E.M.T. Hendrix, Lyle Noakes, W.J. Pullan, G.R. Wood

    Research output: Contribution to journalArticlepeer-review

    7 Citations (Scopus)

    Abstract

    Large scale optimisation problems are frequently solved using stochastic methods. Such methods often generate points randomly in a search region in a neighbourhood of the current point, backtrack to get past barriers and employ a local optimiser. The aim of this paper is to explore how these algorithmic components should be used, given a particular objective function landscape. In a nutshell, we begin to provide rules for efficient travel, if we have some knowledge of the large or small scale geometry.
    Original languageEnglish
    Pages (from-to)579-598
    JournalJournal of Global Optimization
    Volume31
    Issue number4
    DOIs
    Publication statusPublished - 2005

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