Skip to main content

Searching in the presence of noise

  • Theoretical Foundations of Evolutionary Computation
  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1141))

Abstract

In this paper, we examine the effects of noise on both local search and genetic search. Understanding the potential effects of noise on a search space may explain why some search techniques fail and why others succeed in the presence of noise. We discuss two effects that are the result of adding noise to a search space: the annealing of peaks in the search space and the introduction of false local optima.

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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Lashon Booker. Improving Search in Genetic Algorithms. In Lawrence Davis, editor, Genetic Algorithms and Simulated Annealing, chapter 5, pages 61–73. Morgan Kaufmann, 1987.

    Google Scholar 

  2. Lawrence Davis. Bit-Climbing, Representational Bias, and Test Suite Design. In L. Booker and R. Belew, editors, Proc. of the 4th Int'l. Conf. on GAs, pages 18–23. Morgan Kauffman, 1991.

    Google Scholar 

  3. Larry Eshelman. The CHC Adaptive Search Algorithm. How to Have Safe Search When Engaging in Nontraditional Genetic Recombination. In G. Rawlins, editor, FOGA-1, pages 265–283. Morgan Kaufmann, 1991.

    Google Scholar 

  4. J. Michael Fitzpatrick and John Grefenstette. Genetic Algorithm in Noisy Environments. Machine Learning, 3:101–120, 1988.

    Google Scholar 

  5. David Goldberg. A Note on Boltzmann Tournament Selection for Genetic Algorithms and Population-oriented Simulated Annealing. Technical Report Nb. 90003, Department of Engineering Mechanics, University of Alabama, 1990.

    Google Scholar 

  6. Ulrich Hammel and Thomas Bäck. Evolution Strategies on Noisy Functions How to Improve Convergence Properties. In Y. Davidor, H.P. Schwefel, and R. Manner, editors, Parallel Problem Solving from Nature, 3, pages 159–168. Springer/Verlag, 1994.

    Google Scholar 

  7. L. Ingber. Adaptive Simulated Annealing: Lessons Learned. Control and Cybernetics, 25(1):33–54, 1996.

    Google Scholar 

  8. Bennett Levitan and Stuart Kauffman. Adaptive walks with noisy fitness measurements. Molecular Diversity, 1:53–68, 1995.

    Article  PubMed  Google Scholar 

  9. Keith E. Mathias and L. Darrell Whitley. Noisy Function Evaluation and the Delta Coding Algorithm. In Proceedings of the Conference on Neural and Stochastic Methods in Image and Signal Processing III, 1994.

    Google Scholar 

  10. Brad Miller and David Goldberg. Genetic Algorithms, Selection Schemes, and the Varying Effects of Noise. Technical Report IlliGAL Report No. 95009, Department of General Engineering, University of Illinois at Urbana-Champaign, 1995.

    Google Scholar 

  11. Darrell Whitley, Keith Mathias, Soraya Rana, and John Dzubera. Evaluating Evolutionary Algorithms. Artificial Intelligence Journal, 85, August 1996.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Hans-Michael Voigt Werner Ebeling Ingo Rechenberg Hans-Paul Schwefel

Rights and permissions

Reprints and permissions

Copyright information

© 1996 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Rana, S., Whitley, L.D., Cogswell, R. (1996). Searching in the presence of noise. In: Voigt, HM., Ebeling, W., Rechenberg, I., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN IV. PPSN 1996. Lecture Notes in Computer Science, vol 1141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61723-X_984

Download citation

  • DOI: https://doi.org/10.1007/3-540-61723-X_984

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61723-5

  • Online ISBN: 978-3-540-70668-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics