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Matching Stochastic Algorithms to Objective Function Landscapes

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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.

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References

  1. W. Baritompa M. Steel (1996) ArticleTitleBounds on absorption times of directionally biased random sequences Random Structures and Algorithms 9 279–293

    Google Scholar 

  2. Boender C.G.E., Romeijn H.E. (1994). Stochastic Methods In: Horst R., Pardalos P. (ed). Handbook of Global Optimization pp. 829-869. Kluwer Academic Publishers.

  3. E.M.T. Hendrix P.M. Ortigosa I. Garcia (2001) ArticleTitleOn success rates for controlled random search Journal of Global Optimization 21 239–263

    Google Scholar 

  4. Pardalos P., Romeijn E. (2002). Handbook of Global Optimization Vol. 2. Kluwer Academic Publishers.

  5. L. Pronzato E. Walter A. Venot J.F. Lebruchec (1984) ArticleTitleA general purpose global optimizer: Implementation and applications Mathematics and Computers in Simulation 24 412–422

    Google Scholar 

  6. W.J. Pullan (1996) ArticleTitleA direct search method applied to a molecular structure problem Australian Computer Journal 28 113–120

    Google Scholar 

  7. H.E. Romeijn R.L. Smith (1994) ArticleTitleSimulated annealing for constrained global optimization Journal of Global Optimization 5 101–126

    Google Scholar 

  8. F. Schoen (1991) ArticleTitleStochastic techniques for global optimization: A survey of recent advances Journal of Global Optimization 1 207–228

    Google Scholar 

  9. R.L. Smith (1984) ArticleTitleEfficient Monte Carlo procedures for generating points uniformly distributed over bounded regions Operations Research 32 1296–1308

    Google Scholar 

  10. A. Törn M.M. Ali S. Viitanen (1999) ArticleTitleStochastic global optimization: Problem classes and solution techniques Journal of Global Optimization 14 437–447 Occurrence HandleMR1707800

    MathSciNet  Google Scholar 

  11. Törn A., Zilinskas A. (1989). Global Optimization. Lecture Notes in Computer Science Vol. 350. Springer.

  12. D.J. Wales J.P.K. Doye (1997) ArticleTitleGlobal optimization by basin-hopping and the lowest energy structures of Lennard Jones clusters containing up to 110 atoms Journal of Physical Chemistry A 101 5111–5116 Occurrence Handle1:CAS:528:DyaK2sXktVGrurY%3D

    CAS  Google Scholar 

  13. D.J. Wolpert W.G. Macready (1997) ArticleTitleNo free lunch theorems for optimization IEEE Transactions on Evolutionary Computing 1 67–82

    Google Scholar 

  14. Z.B. Zabinsky R.L. Smith J.F. McDonald H.E. Romeijn D.E. Kaufman (1993) ArticleTitleImproving Hit and Run for global optimization Journal of Global Optimization 3 171–192

    Google Scholar 

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Authors and Affiliations

  1. Department of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand

    W. P. Baritompa

  2. Department of Mathematics, Darmstadt University of Technology, D-64289, Darmstadt, Germany

    M. Dür

  3. Group Operations Research and Logistics, Wageningen University, Hollandseweg 1, 6706, KN, Wageningen, The Netherlands

    E. M. T. Hendrix

  4. School of Mathematics and Statistics, University of Western Australia, Nedlands, WA, 6907, Australia

    L. Noakes

  5. School of Information Technology, Griffith University, Gold Coast, Australia

    W. J. Pullan

  6. Department of Statistics, Macquarie University, North Ryde, NSW, 2109, Australia

    G. R. Wood

Authors
  1. W. P. Baritompa
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  2. M. Dür
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  3. E. M. T. Hendrix
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  4. L. Noakes
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  5. W. J. Pullan
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  6. G. R. Wood
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Correspondence to G. R. Wood.

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Baritompa, W.P., Dür, M., Hendrix, E.M.T. et al. Matching Stochastic Algorithms to Objective Function Landscapes. J Glob Optim 31, 579–598 (2005). https://doi.org/10.1007/s10898-004-9968-y

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  • DOI: https://doi.org/10.1007/s10898-004-9968-y

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