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

  • H. Mühlenbein
  • G. Paaß
Theoretical Foundations of Evolutionary Computation
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1141)

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.

Keywords

Genetic Algorithm Conditional Distribution Marginal Distribution Additive Genetic Variance Linkage Equilibrium 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 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. 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. 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. 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. 5.
    W. Buntine. Learning classification trees. Statistics and Computing, 2:63–73, 1992.CrossRefGoogle Scholar
  6. 6.
    J. F. Crow and M. Kimura. An Introduction to Population Genetics Theory. Harper and Row, New York, 1970.Google Scholar
  7. 7.
    D. S. Falconer. Introduction to Quantitative Genetics. Longman, London, 1981.Google Scholar
  8. 8.
    R. A. Fisher. The Genetical Theory of Natural Selection. Dover, New York, 1958.Google Scholar
  9. 9.
    D.E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, 1989.Google Scholar
  10. 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. 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. 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. 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. 14.
    K.V.S. Murthy. On Growing Better Decision Trees from Data. PhD thesis, The John Hopkins University, Baltimore, Maryland, 1995.Google Scholar
  15. 15.
    T. Naglyaki. Introduction to Theoretical Population Genetics. Springer, Berlin, 1992.Google Scholar
  16. 16.
    R.B. Robbins. Some applications of mathematics to breeding problems iii. Genetics, 3:375–389, 1918.Google Scholar
  17. 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. 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

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • H. Mühlenbein
    • 1
  • G. Paaß
    • 1
  1. 1.GMD - Forschungszentrum InformationstechnikSankt AugustinGermany

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