Skip to main content

On the Importance of Information Speed in Structured Populations

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3242))

Abstract

A radius-based separation of selection and recombination spheres in diffusion model EAs is introduced, enabling a new taxonomy, oriented towards information flow analysis. It also contains parallel hillclimbers, panmictic EA and an unexplored area. Experiments are performed systematically on five complex binary and real coded problems in search of the best performing variants w.r.t. available optimization time. Additionally, information flow through recombination and selection is emulated by means of a simple model, that produces qualitative similar results.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   74.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Beyer, H.G., Schwefel, H.P.: Evolution strategies: A comprehensive introduction. Journal Natural Computing 1, 3–52 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  2. Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)

    MATH  Google Scholar 

  3. De Jong, K.A.: An analysis of the behavior of a class of genetic adaptive systems. PhD thesis, University of Michigan (1975)

    Google Scholar 

  4. Goldberg, D.E., Richardson, J.: Genetic algorithms with sharing for multimodal function optimization. In: Grefenstette, J. (ed.) Genetic Algorithms and their Applications (ICGA 1987), pp. 41–49. Lawrence Erlbaum Associates, Mahwah (1987)

    Google Scholar 

  5. Li, J.P., Balazs, M.E., Parks, G.T., Clarkson, P.J.: A species conserving genetic algorithm for multimodal function optimization. Evolutionary Computation 10, 207–234 (2002)

    Article  Google Scholar 

  6. Voudouris, C.: Guided local search – an illustrative example in function optimisation. BT Technology Journal 16, 46–50 (1998)

    Article  Google Scholar 

  7. Lasarczyk, C.W.G., Dittrich, P., Banzhaf, W.: Dynamic subset selection based on a fitness case topology. Evolutionary Computation 12 (2004) (in print)

    Google Scholar 

  8. Alba, E., Tomassini, M.: Parallelism and evolutionary algorithms. IEEE Transactions on Evolutionary Computation 6, 443–462 (2002)

    Article  Google Scholar 

  9. Sarma, J., De Jong, K.: An analysis of the effects of neighborhood size and shape on local selection algorithms. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 236–244. Springer, Heidelberg (1996)

    Chapter  Google Scholar 

  10. Sprave, J.: Linear neighborhood evolution strategy. In: Sebald, A.V., Fogel, L.J. (eds.) Proc. EP1994, pp. 42–51. World Scientific, Singapore (1994)

    Google Scholar 

  11. De Jong, K.A., Potter, M.A., Spears, W.M.: Using problem generators to explore the effects of epistasis. In: Bäck, T. (ed.) ICGA, San Francisco, CA, pp. 338–345. Morgan Kaufmann, San Francisco (1997)

    Google Scholar 

  12. De Jong, K.A., Spears, W.M.: An analysis of the interacting roles of population size and crossover in genetic algorithms. In: Schwefel, H.-P., Männer, R. (eds.) PPSN 1990. LNCS, vol. 496, pp. 38–47. Springer, Heidelberg (1991)

    Chapter  Google Scholar 

  13. Salomon, R.: Re-evaluating genetic algorithm performance under coordinate rotation of benchmark functions: A survey of some theoretical and practical aspects of genetic algorithms. BioSystems 39, 263–278 (1996)

    Article  Google Scholar 

  14. Keane, A.J.: Experiences with optimizers in structural design. In: Parmee, I.C. (ed.) Proc. Adaptive Computing in Engineering Design and Control 1994, Plymouth, UK, pp. 14–27 (1994)

    Google Scholar 

  15. Mitchell, D., Selman, B., Levesque, H.: Hard and easy distributions of SAT problems. In: Proceedings of AAAI–1992, San Jose, California, pp. 459–465. AAAI Press, Menlo Park (1992)

    Google Scholar 

  16. Watts, D.J., Strogatz, S.H.: Collective dynamics of small–world networks. Nature 393, 440–442 (1998)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Preuss, M., Lasarczyk, C. (2004). On the Importance of Information Speed in Structured Populations. In: Yao, X., et al. Parallel Problem Solving from Nature - PPSN VIII. PPSN 2004. Lecture Notes in Computer Science, vol 3242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30217-9_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30217-9_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23092-2

  • Online ISBN: 978-3-540-30217-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics