Strategy adaptation by competing subpopulations

  • Dirk Schlierkamp-Voosen
  • Heinz Mühlenbein
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 866)


The breeder genetic algorithm BGA depends on a set of control parameters and genetic operators. In this paper it is shown that strategy adaptation by competing subpopulations makes the BGA more robust and more efficient. Each subpopulation uses a different strategy which competes with other subpopulations. Numerical results are presented for a number of test functions.


breeder genetic algorithm strategy adaptation competition multiresolution search 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Thomas Bäck. Self-Adaption in Genetic Algorithms. In Francisco Varela and Paul Bourgine, editors, Towards a Practice of Autonomous Systems, pages 263–271, 1992.Google Scholar
  2. 2.
    Thomas Bäck and Hans-Paul Schwefel. A Survey of Evolution Strategies. In Proceedings of the Fourth International Conference of Genetic Algorithms, pages 2–9, San Diego, 1991. ICGA.Google Scholar
  3. 3.
    Thomas Bäck and Hans-Paul Schwefel. An Overview of Evolutionary Algorithms for Parameter Optimization. Evolutionary Computation, 1:1–24, 1993.Google Scholar
  4. 4.
    Terence C. Fogarty. Varying the probability of Mutation in the Genetic Algorithm. In J. David Schaffer, editor, Proceedings of the Third International Conference of Genetic Algorithms, pages 104–109. Morgan-Kaufman, 1989.Google Scholar
  5. 5.
    Michael Herdy. Reproductive Isolation as Strategy Parameter in Hierarchical Organized Evolution Strategies. In PPSN 2 Bruxelles, pages 207–217, September 1992.Google Scholar
  6. 6.
    Heinz Mühlenbein and Dirk Schlierkamp-Voosen. Predictive Models for the Breeder Genetic Algorithm: Continuous Parameter Optimization. Evolutionary Computation, 1(1):25–49, 1993.Google Scholar
  7. 7.
    Heinz Mühlenbein and Dirk Schlierkamp-Voosen. The science of breeding and its application to the breeder genetic algorithm. Evolutionary Computation, 1(4):335–360, 1994.Google Scholar
  8. 8.
    H. H. Rosenbrock. An automatic method for finding the greatest or least value of a function. The Computer Journal, 3(3):175–184, 10 1960.CrossRefGoogle Scholar
  9. 9.
    Fabio Schoen. A Wide Class of Test Functions for Global Optimization. Journal of Global Optimization, 3(2):133–137, 1993.CrossRefGoogle Scholar
  10. 10.
    H.-P. Schwefel. Numerical Optimization of Computer Models. Wiley, Chichester, 1981.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • Dirk Schlierkamp-Voosen
    • 1
  • Heinz Mühlenbein
    • 1
  1. 1.GMD Schloß BirlinghovenSankt AugustinGermany

Personalised recommendations