Skip to main content
SpringerLink
Log in

From recombination of genes to the estimation of distributions I. Binary parameters

  • Theoretical Foundations of Evolutionary Computation
  • Conference paper
  • First Online:
Parallel Problem Solving from Nature — PPSN IV (PPSN 1996)

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

Included in the following conference series:

Abstract

The Breeder Genetic Algorithm (BGA) is based on the equation for the response to selection. In order to use this equation for prediction, the variance of the fitness of the population has to be estimated. For the usual sexual recombination the computation can be difficult. In this paper we shortly state the problem and investigate several modifications of sexual recombination. The first method is gene pool recombination, which leads to marginal distribution algorithms. In the last part of the paper we discuss more sophisticated methods, based on estimating the distribution of promising points.

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. H. Asoh and H. Mühlenbein. Estimating the heritability by decomposing the genetic variance. In Y. Davidor, H.-P. Schwefel, and R. Männer, editors, Parallel Problem Solving from Nature, Lecture Notes in Computer Science 866, pages 98–107. Springer-Verlag, 1994.

    Google Scholar 

  2. S. Baluja and R. Caruana. Removing the genetics from the standard genetic algorithm. In Proc. of the 12th Intern. Conf. on Machine Learning, Lake Tahoe, 1995.

    Google Scholar 

  3. J. Besag. Spatial interaction and the statistical analysis of lattice systems. J. Royal Statistical Society, Series B, pages 192–236, 1974.

    Google Scholar 

  4. L. Breiman, J.H. Friedman, R. Olshen, and C.J. Stone. Classification and Regression Trees. Wadsworth International Group, Belmont, California, 1984.

    Google Scholar 

  5. W. Buntine. Learning classification trees. Statistics and Computing, 2:63–73, 1992.

    Article  Google Scholar 

  6. J. F. Crow and M. Kimura. An Introduction to Population Genetics Theory. Harper and Row, New York, 1970.

    Google Scholar 

  7. D. S. Falconer. Introduction to Quantitative Genetics. Longman, London, 1981.

    Google Scholar 

  8. R. A. Fisher. The Genetical Theory of Natural Selection. Dover, New York, 1958.

    Google Scholar 

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

    Google Scholar 

  10. D.E. Goldberg, K. Deb, H. Kargupta, and G. Harik. Rapid, accurate optimization of difficult problems using fast messy genetic algorithms. In S. Forrest, editor, Proc. of the Fith Int. Conf. on Genetic Algorithms, pages 56–64, San Mateo, 1993. Morgan-Kaufman.

    Google Scholar 

  11. V. Kvasnicka, M. Pelikan, and J. Pospichal. Hill-climbing with learning: An abstraction of genetic algorithm. Technical report, Slovak Technical University, Bratislava, 1995.

    Google Scholar 

  12. H. Mühlenbein and D. Schlierkamp-Voosen. The science of breeding and its application to the breeder genetic algorithm. Evolutionary Computation, 1:335–360, 1994.

    Google Scholar 

  13. H. Mühlenbein and H.-M. Voigt. Gene pool recombination in genetic algorithms. In J.P. Kelly and I.H Osman, editors, Metaheuristics: Theory and Applications, Norwell, 1996. Kluwer Academic Publisher.

    Google Scholar 

  14. K.V.S. Murthy. On Growing Better Decision Trees from Data. PhD thesis, The John Hopkins University, Baltimore, Maryland, 1995.

    Google Scholar 

  15. T. Naglyaki. Introduction to Theoretical Population Genetics. Springer, Berlin, 1992.

    Google Scholar 

  16. R.B. Robbins. Some applications of mathematics to breeding problems iii. Genetics, 3:375–389, 1918.

    Google Scholar 

  17. J.N. Sonquist and J.N. Morgan. The Detection of Interaction Effects, volume Monograph 35. Survey Research Center, Institute for Social Research, University of Michigan, 1964.

    Google Scholar 

  18. G. Syswerda. Uniform crossover in genetic algorithms. In H. Schaffer, editor, 3rd Int. Conf. on Genetic Algorithms, pages 2–9, San Mateo, 1989. Morgan Kaufmann.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. GMD - Forschungszentrum Informationstechnik, 53754, Sankt Augustin, Germany

    H. Mühlenbein & G. Paaß

Authors
  1. H. Mühlenbein
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. G. Paaß
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Mühlenbein, H., Paaß, G. (1996). From recombination of genes to the estimation of distributions I. Binary parameters. 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_982

Download citation

  • DOI: https://doi.org/10.1007/3-540-61723-X_982

  • 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

Search

Navigation