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Parameter Meta-optimization of Metaheuristic Optimization Algorithms

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 6927)

Abstract

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.

Keywords

  • 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.

The work described in this paper was done within the Josef Ressel Centre for Heuristic Optimization Heureka! ( http://heureka.heuristiclab.com/ ) sponsored by the Austrian Research Promotion Agency (FFG).

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References

  1. Beyer, H.G., Schwefel, H.P.: Evolution strategies - A comprehensive introduction. Natural Computing 1(1), 3–52 (2002)

    MathSciNet  CrossRef  MATH  Google Scholar 

  2. Deb, K., Agrawal, R.B.: Simulated binary crossover for continuous search space. Complex Systems 9, 115–148 (1995)

    MathSciNet  MATH  Google Scholar 

  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. Dumitrescu, D., Lazzerini, B., Jain, L.C., Dumitrescu, A.: Evolutionary Computation. CRC Press, Boca Raton (2000)

    MATH  Google Scholar 

  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. 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. Grefenstette, J.: Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics 16(1), 122–128 (1986)

    CrossRef  Google Scholar 

  8. Griewank, A.O.: Generalized descent for global optimization. Journal of Optimization Theory and Applications 34, 11–39 (1981)

    MathSciNet  CrossRef  MATH  Google Scholar 

  9. Mercer, R., Sampson, J.: Adaptive search using a reproductive metaplan. Kybernetes 7(3), 215–228 (1978)

    CrossRef  Google Scholar 

  10. Mühlenbein, H., Schlierkamp-Voosen, D.: Predictive models for the breeder genetic algorithm i. continuous parameter optimization. Evolutionary Computation 1(1), 25–49 (1993)

    CrossRef  Google Scholar 

  11. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Heidelberg (1999)

    MATH  Google Scholar 

  12. Pedersen, E.M.H.: Tuning & Simplifying Heuristical Optimization. Ph.D. thesis, University of Southampton (2010)

    Google Scholar 

  13. Schwefel, H.P.P.: Evolution and Optimum Seeking: The Sixth Generation. John Wiley & Sons, Inc., Chichester (1993)

    Google Scholar 

  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. 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. 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. 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. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1), 67–82 (1997)

    CrossRef  Google Scholar 

  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 

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Neumüller, C., Wagner, S., Kronberger, G., Affenzeller, M. (2012). Parameter Meta-optimization of Metaheuristic Optimization Algorithms. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2011. EUROCAST 2011. Lecture Notes in Computer Science, vol 6927. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27549-4_47

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  • DOI: https://doi.org/10.1007/978-3-642-27549-4_47

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27548-7

  • Online ISBN: 978-3-642-27549-4

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