Skip to main content
SpringerLink
Log in

Benchmarking Evolutionary and Hybrid Algorithms Using Randomized Self-similar Landscapes

  • Conference paper
Simulated Evolution and Learning (SEAL 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4247))

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. MacNish, C.: Huygens benchmarking suite (2006), http://gungurru.csse.uwa.edu.au/cara/huygens/

  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. 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. Spears, W.M.: Genetic Algorithms (Evolutionary Algorithms): Repository of test functions (2006), http://www.cs.uwyo.edu/~wspears/functs.html

  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. 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)

    Article  MATH  MathSciNet  Google Scholar 

  7. Wolpert, D.H., Macready, W.G.: No Free Lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1, 67–82 (1997)

    Article  Google Scholar 

  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. Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C, 2nd edn. CUP (1992)

    Google Scholar 

  10. W3C XML Protocol Working Group: SOAP Version 1.2 (2006), http://www.w3.org/TR/soap12-part0/

Download references

Author information

Authors and Affiliations

  1. University of Western Australia, Perth, WA, 6009, Australia

    Cara MacNish

Authors
  1. Cara MacNish
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering and Management, Cheng Shiu University, Kaohsiung County Taiwan, ROC

    Tzai-Der Wang

  2. School of Computer Science and information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Xiaodong Li

  3. AI-ECON Research Center, Department of Economics, National Chengchi University, 11623, Taipei, Taiwan

    Shu-Heng Chen

  4. Department of Computer Science and Technology, University of Sci. & Tech. of China, 230026, Hefei, Anhui, P.R. China

    Xufa Wang

  5. School of Information Technology and Electrical Engineering, University of New South Wales, Australian Defence Force Academy, Canberra, Australia

    Hussein Abbass

  6. The Univertity of Tokyo, Japan

    Hitoshi Iba

  7. Department of Computer, University of Science and Technology of China, 230027, Hefei, China

    Guo-Liang Chen

  8. University of Birmingham, Birmingham, UK

    Xin Yao

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

MacNish, C. (2006). Benchmarking Evolutionary and Hybrid Algorithms Using Randomized Self-similar Landscapes. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_46

Download citation

  • DOI: https://doi.org/10.1007/11903697_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-47331-2

  • Online ISBN: 978-3-540-47332-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation