Abstract
On-line control of EA parameters is an approach to parameter setting that offers the advantage of values changing during the run. In this paper, we investigate parameter control from a generic and parameter-independent perspective. We propose a generic control mechanism that is targeted to repetitive applications, can be applied to any numeric parameter and is tailored to specific types of problems through an off-line calibration process. We present proof-of-concept experiments using this mechanism to control the mutation step size of an Evolutionary Strategy (ES). Results show that our method is viable and performs very well, compared to the tuning approach and traditional control methods.
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References
Bäck, T.: Evolutionary Algorithms in Theory and Practice. Oxford University Press, Oxford (1996)
De Jong, K.: Parameter Setting in EAs: a 30 Year Perspective. In: Lobo, F., Lima, C., Michalewicz, Z. (eds.) Parameter Setting in Evolutionary Algorithms. SCI, vol. 54, pp. 1–18. Springer, Heidelberg (2007)
di Tollo, G., Lardeux, F., Maturana, J., Saubion, F.: From Adaptive to More Dynamic Control in Evolutionary Algorithms. In: Hao, J.-K. (ed.) EvoCOP 2011. LNCS, vol. 6622, pp. 130–141. Springer, Heidelberg (2011)
Eiben, A.E., Hinterding, R., Michalewicz, Z.: Parameter Control in Evolutionary Algorithms. IEEE Transactions on Evolutionary Computation 3(2), 124–141 (1999)
Eiben, A.E., Michalewicz, Z., Schoenauer, M., Smith, J.E.: Parameter Control in Evolutionary Algorithms. In: Lobo, F., Lima, C., Michalewicz, Z. (eds.) Parameter Setting in Evolutionary Algorithms. SCI, vol. 54, pp. 19–46. Springer, Heidelberg (2007)
Fogarty, T.C.: Varying the probability of mutation in the genetic algorithm. In: Proceedings of the Third International Conference on Genetic Algorithms, pp. 104–109. Morgan Kaufmann Publishers Inc., San Francisco (1989)
Karafotias, G., Haasdijk, E., Eiben, A.E.: An algorithm for distributed on-line, on-board evolutionary robotics. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO 2011, pp. 171–178. ACM (2011)
Lee, M.A., Takagi, H.: Dynamic control of genetic algorithms using fuzzy logic techniques. In: Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 76–83. Morgan Kaufmann (1993)
Majig, M., Fukushima, M.: Adaptive fitness function for evolutionary algorithm and its applications. In: International Conference on Informatics Research for Development of Knowledge Society Infrastructure, pp. 119–124 (2008)
Maturana, J., Saubion, F.: On the Design of Adaptive Control Strategies for Evolutionary Algorithms. In: Monmarché, N., Talbi, E.-G., Collet, P., Schoenauer, M., Lutton, E. (eds.) EA 2007. LNCS, vol. 4926, pp. 303–315. Springer, Heidelberg (2008)
Nannen, V., Smit, S., Eiben, A.E.: Costs and Benefits of Tuning Parameters of Evolutionary Algorithms. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 528–538. Springer, Heidelberg (2008)
Rechenberg, I.: Evolutionstrategie: Optimierung Technisher Systeme nach Prinzipien des Biologischen Evolution. Fromman-Hozlboog Verlag, Stuttgart (1973)
Schraudolph, N.N., Belew, R.K.: Dynamic parameter encoding for genetic algorithms. Machine Learning 9, 9–21 (1992)
Smit, S., Eiben, A.E.: Multi-problem parameter tuning using bonesa. In: Hao, J., Legrand, P., Collet, P., Monmarché, N., Lutton, E., Schoenauer, M. (eds.) Artificial Evolution, pp. 222–233 (2011)
Smit, S.K., Szláavik, Z., Eiben, A.E.: Population diversity index: a new measure for population diversity. In: GECCO (Companion), pp. 269–270 (2011)
Smith, R., Smuda, E.: Adaptively resizing populations: Algorithm, analysis and first results. Complex Systems 9(1), 47–72 (1995)
Spears, W.M.: Adapting crossover in evolutionary algorithms. In: Proceedings of the Fourth Annual Conference on Evolutionary Programming, pp. 367–384. MIT Press (1995)
Vajda, P., Eiben, A.E., Hordijk, W.: Parameter Control Methods for Selection Operators in Genetic Algorithms. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN X 2008. LNCS, vol. 5199, pp. 620–630. Springer, Heidelberg (2008)
Wong, Y.-Y., Lee, K.-H., Leung, K.-S., Ho, C.-W.: A novel approach in parameter adaptation and diversity maintenance for genetic algorithms. Soft Computing - A Fusion of Foundations, Methodologies and Applications 7, 506–515 (2003)
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Karafotias, G., Smit, S.K., Eiben, A.E. (2012). A Generic Approach to Parameter Control. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2012. Lecture Notes in Computer Science, vol 7248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29178-4_37
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DOI: https://doi.org/10.1007/978-3-642-29178-4_37
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