Short notes on the schema theorem and the building block hypothesis in genetic algorithms | SpringerLink
Skip to main content
Springer Nature Link
Log in

Short notes on the schema theorem and the building block hypothesis in genetic algorithms

  • Conference paper
  • First Online:
Evolutionary Programming VII (EP 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1447))

Included in the following conference series:

Abstract

After decades of success, research on evolutionary algorithms aims at developing a sound theory that describes and predict the behavior of these algorithms. One research topic of interest is the analysis of the role of crossover and recombination in genetic algorithms, especially since various papers come to different conclusions. The goals of this paper are to revisit some well-known concepts and to discuss some new aspects that might be helpful for further clarification.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. L. Altenberg. The Schema Theorem and the Price's Theorem, In: L.D. Whitley and M.D. Vose (Eds.) Foundations of Genetic Algorithms 3, 23–49, 1995. Morgan Kaufmann, San Mateo, CA.

    Google Scholar 

  2. T. Bäck and H.-P. Schwefel. An Overview of Evolutionary Algorithms for Parameter Optimization. Evolutionary Computation. 1(1):1–23, 1993.

    Google Scholar 

  3. H.-G. Beyer. Toward a Theory of Evolution Strategies: the (μ, λ)-Theory. Evolutionary Computation. 2(4):381–407, 1995.

    Google Scholar 

  4. H.-G. Beyer. Toward a Theory of Evolution Strategies: on the Benefit of Sex — the (μ/μ, λ)-Theory. Evolutionary Computation. 3(1):81–110, 1995.

    Google Scholar 

  5. H.-G. Beyer. An Alternative Explanation for the Manner in which Genetic Algorithms Operate. BioSystems. 41:1–15, 1997.

    Google Scholar 

  6. K.A. De Jong. An Analysis of the Behavior of a Class of Genetic Adaptive Systems. Ph.D. Thesis, University of Michigan, 1975.

    Google Scholar 

  7. D. Floreano, and F. Mondada. Evolution of Homing Navigation in a Real Mobile Robot. IEEE Transactions on Systems, Man, and Cybernetics-Part B. 26(3):396–407, 1996.

    Google Scholar 

  8. D.B. Fogel. Evolutionary Computation: Toward a New Philosophy of Machine Learning Intelligence, IEEE Press, NJ, 1995.

    Google Scholar 

  9. L.J. Fogel. Autonomous Automata, Industrial Research. 4:14–19, 1962.

    Google Scholar 

  10. D.E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, MA, 1989.

    Google Scholar 

  11. J.J. Grefenstette and J.E. Baker. How Genetic Algorithms Work: A Critical Look at Implicit Parallelism. In: J.D. Schaffer (Ed.) Proceedings of the International Conference on Genetic Algorithms ICGA3, 20–27, 1989. Morgan Kaufmann, San Mateo, CA.

    Google Scholar 

  12. J.H. Holland. Adaptation in Natural and Artificial Systems. MIT Press, Cambridge, MA, 1992.

    Google Scholar 

  13. H. Mühlenbein and D. Schlierkamp-Voosen. Predictive Models for the Breeder Genetic Algorithm I. Evolutionary Computation. 1(1):25–50, 1993.

    Google Scholar 

  14. I. Rechenberg. Evolutionsstrategie. Frommann-Holzboog, Stuttgart, 1973.

    Google Scholar 

  15. R. Salomon. Reevaluating Genetic Algorithm Performance under Coordinate Rotation of Benchmark Functions; A survey of some theoretical and practical aspects of genetic algorithms. BioSystems. 39(3):263–278, 1996.

    Google Scholar 

  16. R. Salomon. The Influence of Different Coding Schemes on the Computational Complexity of Genetic Algorithms in Function Optimization. In: H.-M. Voigt, W. Ebeling, I. Rechenberg, and H.-P. Schwefel, (eds.), Proceedings of The Fourth International Conference on Parallel Problem Solving from Nature (PPSN IV), 227–235, 1996. Springer-Verlag, Berlin.

    Google Scholar 

  17. H.-P. Schwefel. Evolution and Optimum Seeking. John Wiley and Sons, NY, 1995.

    Google Scholar 

  18. M. Srinivas and L. Patnaik. Genetic Algorithms: A Survey. Computer. 27(6):17–26, 1994.

    Google Scholar 

  19. D. Thierens and D.E. Goldberg. Convergence Models of Genetic Algorithm Selection Schemes. In: Y. Davidor, H.P. Schwefel, and R. Männer (eds.), Proceedings of Parallel Problem Solving from Nature 3, 119–129, 1994. Springer-Verlag, Berlin.

    Google Scholar 

  20. M.D. Vose. Generalizing the notion of schema in genetic algorithms. Artificial Intelligence. 50:385–396, 1991.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland

    Ralf Salomon

Authors
  1. Ralf Salomon
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

V. W. Porto N. Saravanan D. Waagen A. E. Eiben

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Salomon, R. (1998). Short notes on the schema theorem and the building block hypothesis in genetic algorithms. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds) Evolutionary Programming VII. EP 1998. Lecture Notes in Computer Science, vol 1447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0040765

Download citation

  • DOI: https://doi.org/10.1007/BFb0040765

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64891-8

  • Online ISBN: 978-3-540-68515-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation