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Journal of Global Optimization

, Volume 31, Issue 4, pp 579–598 | Cite as

Matching Stochastic Algorithms to Objective Function Landscapes

  • W. P. Baritompa
  • M. Dür
  • E. M. T. Hendrix
  • L. Noakes
  • W. J. Pullan
  • G. R. WoodEmail author
Article

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.

Keywords

Backtracking Global optimisation Local optimisation Search region Simulated annealing Temperature 

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Copyright information

© Springer 2005

Authors and Affiliations

  • W. P. Baritompa
    • 1
  • M. Dür
    • 2
  • E. M. T. Hendrix
    • 3
  • L. Noakes
    • 4
  • W. J. Pullan
    • 5
  • G. R. Wood
    • 6
    Email author
  1. 1.Department of Mathematics and StatisticsUniversity of CanterburyChristchurchNew Zealand
  2. 2.Department of MathematicsDarmstadt University of TechnologyDarmstadtGermany
  3. 3.Group Operations Research and LogisticsWageningen UniversityWageningenThe Netherlands
  4. 4.School of Mathematics and StatisticsUniversity of Western AustraliaNedlandsAustralia
  5. 5.School of Information TechnologyGriffith UniversityGold CoastAustralia
  6. 6.Department of StatisticsMacquarie UniversityNorth RydeAustralia

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