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Benchmarking Evolutionary and Hybrid Algorithms Using Randomized Self-similar Landscapes

  • Cara MacNish
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)

Abstract

The success (and potential success) of evolutionary algorithms and their hybrids on difficult real-valued optimization problems has led to an explosion in the number of algorithms and variants proposed. This has made it difficult to definitively compare the range of algorithms proposed, and therefore to advance the field.

In this paper we discuss the difficulties of providing widely available benchmarking, and present a solution that addresses these difficulties. Our solution uses automatically generated fractal landscapes, and allows user’s algorithms written in any language and run on any platform to be “plugged into” the benchmarking software via the web.

Keywords

real-valued optimisation evolutionary algorithms hybrid algorithms benchmarking 

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References

  1. 1.
    MacNish, C.: Huygens benchmarking suite (2006), http://gungurru.csse.uwa.edu.au/cara/huygens/
  2. 2.
    De Jong, K.A.: Analysis of the behaviour of a class of genetic adaptive systems. PhD thesis, University of Michigan, Ann Arbor, MI (1975)Google Scholar
  3. 3.
    Schaffer, J.D., Caruana, R.A., Eshelman, L.J., Das, R.: A study of control parameters affecting online performance of genetic algorithms for function optimization. In: Schaffer, J.D. (ed.) Proc. 3rd International Conference on Genetic Algorithms, pp. 51–60. Morgan Kaufmann, San Francisco (1989)Google Scholar
  4. 4.
    Spears, W.M.: Genetic Algorithms (Evolutionary Algorithms): Repository of test functions (2006), http://www.cs.uwyo.edu/~wspears/functs.html
  5. 5.
    Spears, W.M., Potter, M.A.: Genetic Algorithsm (Evolutionary Algorithms): Repository of test problem generators (2006), http://www.cs.uwyo.edu/~wspears/generators.html
  6. 6.
    Igel, C., Toussaint, M.: A no-free-lunch theorem for non-uniform distributions of target functions. J. Mathematical Modelling and Algorithms 3, 313–322 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Wolpert, D.H., Macready, W.G.: No Free Lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1, 67–82 (1997)CrossRefGoogle Scholar
  8. 8.
    MacNish, C.: Benchmarking evolutionary and hybrid algorithms using randomised self-similar landscapes: Extended version. Technical report, University of Western Australia, School of Computer Science & Software Engineering (2006)Google Scholar
  9. 9.
    Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C, 2nd edn. CUP (1992)Google Scholar
  10. 10.
    W3C XML Protocol Working Group: SOAP Version 1.2 (2006), http://www.w3.org/TR/soap12-part0/

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Cara MacNish
    • 1
  1. 1.University of Western AustraliaPerthAustralia

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