Is Self-adaptation of Selection Pressure and Population Size Possible? – A Case Study

  • A. E. Eiben
  • M. C. Schut
  • A. R. de Wilde
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4193)


In this paper we seek an answer to the following question: Is it possible and rewarding to self-adapt parameters regarding selection and population size in an evolutionary algorithm? The motivation comes from the observation that the majority of the existing EC literature is concerned with (self-)adaptation of variation operators, while there are indications that (self-)adapting selection operators or the population size can be equally or even more rewarding. We approach the question in an empirical manner. We design and execute experiments for comparing the performance increase of a benchmark EA when augmented with self-adaptive control of parameters concerning selection and population size in isolation and in combination. With the necessary caveats regarding the test suite and the particular mechanisms used we observe that self-adapting selection yields the highest benefit (up to 30-40%) in terms of speed.


Population Size Evolutionary Algorithm Selection Pressure Global Parameter Tournament Size 
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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  1. 1.
    Aarts, E.H.L., Korst, J.: Simulated Annealing and Boltzmann Machines. Wiley, Chichester (1989)MATHGoogle Scholar
  2. 2.
    Arabas, J., Michalewicz, Z., Mulawka, J.: GAVaPS - A Genetic Algorithm with Varying Population Size. In: Proceedings of the First IEEE Conference on Evolutionary Computation, pp. 73–78. IEEE Press, Piscataway (1994)CrossRefGoogle Scholar
  3. 3.
    Bäck, T.: Evolutionary Algorithms in Theory and Practice. Oxford University Press, New York (1996)MATHGoogle Scholar
  4. 4.
    Bäck, T., Eiben, A.E., van der Vaart, N.A.L.: An empirical study on GAs ”without parameters”. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 315–324. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  5. 5.
    Bäck, T., Schütz, M.: Intelligent mutation rate control in canonical genetic algorithms. In: Michalewicz, M., Raś, Z.W. (eds.) ISMIS 1996. LNCS, vol. 1079, pp. 158–167. Springer, Heidelberg (1996)Google Scholar
  6. 6.
    Costa, J., Tavares, R., Rosa, A.: An experimental study on dynamic random variation of population size. In: Proc. IEEE Systems, Man and Cybernetics Conf., Tokyo, vol. 6, pp. 607–612. IEEE Press, Los Alamitos (1999)Google Scholar
  7. 7.
    Dukkipati, A., Musti, N.M., Bhatnagar, S.: Cauchy annealing schedule: An annealing schedule for boltzmann selection scheme in evolutionary algorithms. In: Proceedings of the 2004 IEEE Congress on Evolutionary Computation, pp. 55–62 (2004)Google Scholar
  8. 8.
    Eiben, A.E., Hinterding, R., Michalewicz, Z.: Parameter Control in Evolutionary Algorithms. IEEE Transactions on Evolutionary Computation 3(2), 124–141 (1999)CrossRefGoogle Scholar
  9. 9.
    Eiben, A.E., Marchiori, E., Valko, V.A.: Evolutionary Algorithms with on-the-fly Population Size Adjustment. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 41–50. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)MATHGoogle Scholar
  11. 11.
    Gonzalez, L., Cannady, J.: A self-adaptive negative selection approach for anomaly detection. In: 2004 Congress on Evolutionary Computation (CEC 2004), pp. 1561–1568. IEEE Press, Piscataway (2004)Google Scholar
  12. 12.
    Harik, G.R., Lobo, F.G.: A parameter-less genetic algorithm. In: Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M., Smith, R.E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, Orlando, Florida, USA, vol. 1, pp. 258–265. Morgan Kaufmann, San Francisco (1999)Google Scholar
  13. 13.
    Hinterding, R., Michalewicz, Z., Peachey, T.C.: Self-adaptive genetic algorithm for numeric functions. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 420–429. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  14. 14.
    Lobo, F.G.: The parameter-less Genetic Algorithm: rational and automated parameter selection for simplified Genetic Algorithm operation. PhD thesis, Universidade de Lisboa (2000)Google Scholar
  15. 15.
    Poupaert, E., Deville, Y.: Acceptance driven local search and evolutionary algorithms. In: Spector, L., Goodman, E., Wu, A., Langdon, W.B., Voigt, H.-M., Gen, M., Sen, S., Dorigo, M., Pezeshk, S., Garzon, M., Burke, E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001), pp. 1173–1180. Morgan Kaufmann, San Francisco (2001)Google Scholar
  16. 16.
    Schlierkamp-Voosen, D., Mühlenbein, H.: Adaptation of population sizes by competing subpopulations. In: Proceedings of the 1996 IEEE Conference on Evolutionary Computation, IEEE Press, Piscataway (1996)Google Scholar
  17. 17.
    Schwefel, H.-P.: Evolution and Optimum Seeking. Wiley, Chichester (1995)Google Scholar
  18. 18.
    Smith, R.E.: Adaptively resizing populations: An algorithm and analysis. In: Forrest, S. (ed.) Proceedings of the 5th International Conference on Genetic Algorithms, Morgan Kaufmann, San Francisco (1993)Google Scholar
  19. 19.
    Spears, W.M.: Evolutionary Algorithms: the role of mutation and recombination. Springer, Berlin, Heidelberg, New York (2000)MATHGoogle Scholar
  20. 20.
    Zitzler, E., Künzli, S.: Indicator-based selection in multiobjective search. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 832–842. Springer, Heidelberg (2004)CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • A. E. Eiben
    • 1
  • M. C. Schut
    • 1
  • A. R. de Wilde
    • 1
  1. 1.Department of Computer ScienceVrije Universiteit Amsterdam 

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