Matching Stochastic Algorithms to Objective Function Landscapes

  • Authors:

  • W. P. Baritompa;M. Dür;E. M. Hendrix;L. Noakes;W. J. Pullan;G. R. Wood

  • Affiliations:

  • Department of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand;Department of Mathematics, Darmstadt University of Technology, Darmstadt, Germany D-64289;Group Operations Research and Logistics, Wageningen University, Wageningen, The Netherlands 6706;School of Mathematics and Statistics, University of Western Australia, Nedlands, Australia 6907;School of Information Technology, Griffith University, Gold Coast, Australia;Department of Statistics, Macquarie University, North Ryde, Australia 2109

  • Venue:
  • Journal of Global Optimization
  • Year:
  • 2005

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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.