Matching Stochastic Algorithms to Objective Function Landscapes

B. Baritompa, M. Dür, E.M.T. Hendrix, L. Noakes, W.J. Pullan, G.R. Wood

Research output: Contribution to journalArticleAcademicpeer-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

Keywords

  • global optimization

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