Parameter Meta-optimization of Metaheuristic Optimization Algorithms

  • Christoph Neumüller
  • Stefan Wagner
  • Gabriel Kronberger
  • Michael Affenzeller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6927)


The quality of a heuristic optimization algorithm is strongly dependent on its parameter values. Finding the optimal parameter values is a laborious task which requires expertise and knowledge about the algorithm, its parameters and the problem. This paper describes, how the optimization of parameters can be automated by using another optimization algorithm on a meta-level. To demonstrate this, a meta-optimization problem which is algorithm independent and allows any kind of algorithm on the meta- and base-level is implemented for the open source optimization environment HeuristicLab. Experimental results of the optimization of a genetic algorithm for different sets of base-level problems with different complexities are shown.


Genetic Algorithm Search Range Evolutionary Algo Heuristic Optimization Algorithm Real Parameter Optimization 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Beyer, H.G., Schwefel, H.P.: Evolution strategies - A comprehensive introduction. Natural Computing 1(1), 3–52 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Deb, K., Agrawal, R.B.: Simulated binary crossover for continuous search space. Complex Systems 9, 115–148 (1995)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Deb, K., Goyal, M.: A combined genetic adaptive search (geneas) for engineering design. Computer Science and Informatics 26, 30–45 (1996)Google Scholar
  4. 4.
    Dumitrescu, D., Lazzerini, B., Jain, L.C., Dumitrescu, A.: Evolutionary Computation. CRC Press, Boca Raton (2000)zbMATHGoogle Scholar
  5. 5.
    Eiben, A.E., Michalewicz, Z., Schoenauer, M., Smith, J.E.: Parameter control in evolutionary algorithms. IEEE Transactions on Evolutionary Computation (1999)Google Scholar
  6. 6.
    English, T.M.: Evaluation of evolutionary and genetic optimizers: No free lunch. In: Evolutionary Programming V: Proceedings of the Fifth Annual Conference on Evolutionary Programming, pp. 163–169. MIT Press, Cambridge (1996)Google Scholar
  7. 7.
    Grefenstette, J.: Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics 16(1), 122–128 (1986)CrossRefGoogle Scholar
  8. 8.
    Griewank, A.O.: Generalized descent for global optimization. Journal of Optimization Theory and Applications 34, 11–39 (1981)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Mercer, R., Sampson, J.: Adaptive search using a reproductive metaplan. Kybernetes 7(3), 215–228 (1978)CrossRefGoogle Scholar
  10. 10.
    Mühlenbein, H., Schlierkamp-Voosen, D.: Predictive models for the breeder genetic algorithm i. continuous parameter optimization. Evolutionary Computation 1(1), 25–49 (1993)CrossRefGoogle Scholar
  11. 11.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Heidelberg (1999)zbMATHGoogle Scholar
  12. 12.
    Pedersen, E.M.H.: Tuning & Simplifying Heuristical Optimization. Ph.D. thesis, University of Southampton (2010)Google Scholar
  13. 13.
    Schwefel, H.P.P.: Evolution and Optimum Seeking: The Sixth Generation. John Wiley & Sons, Inc., Chichester (1993)Google Scholar
  14. 14.
    Smit, S.K., Eiben, A.E.: Comparing parameter tuning methods for evolutionary algorithms. In: Proceedings of the Eleventh Conference on Congress on Evolutionary Computation, pp. 399–406 (2009)Google Scholar
  15. 15.
    Takahashi, M., Kita, H.: A crossover operator using independent component analysis for real-coded genetic algorithms. In: Proceedings of the 2001 Congress on Evolutionary Computation, pp. 643–649 (2001)Google Scholar
  16. 16.
    Wagner, S.: Heuristic Optimization Software Systems - Modeling of Heuristic Optimization Algorithms in the HeuristicLab Software Environment. Ph.D. thesis, Johannes Kepler University, Linz, Austria (2009)Google Scholar
  17. 17.
    Wagner, S., Affenzeller, M.: SexualGA: Gender-specific selection for genetic algorithms. In: Proceedings of the 9th World Multi-Conference on Systemics, Cybernetics and Informatics (WMSCI) 2005, vol. 4, pp. 76–81. International Institute of Informatics and Systemics (2005)Google Scholar
  18. 18.
    Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1), 67–82 (1997)CrossRefGoogle Scholar
  19. 19.
    Wright, A.H.: Genetic algorithms for real parameter optimization. In: Foundations of Genetic Algorithms, pp. 205–218. Morgan Kaufmann, San Francisco (1991)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Christoph Neumüller
    • 1
  • Stefan Wagner
    • 1
  • Gabriel Kronberger
    • 1
  • Michael Affenzeller
    • 1
  1. 1.Heuristic and Evolutionary Algorithms Laboratory School of Informatics, Communications and MediaUpper Austria University of Applied SciencesHagenbergAustria

Personalised recommendations