Abstract
Operator adaptation in evolutionary programming is investigated from both population level and individual level in this paper. The updating rule for operator adaptation is defined based on the fitness distributions at population level compared to the immediate reward or punishment from the feedback of mutations at individual level. Through observing the behaviors of operator adaptation in evolutionary programming, it is discovered that a small-stepping operator could become a dominant operator when other operators have rather larger step sizes. Therefore, it is possible that operator adaptation could lead to slow evolution when operators are adapted freely by themselves.
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References
Meyer-Nieberg, S., Beyer, H.-G.: Self-Adaptation in Evolutionary Algorithms. In: Lobo, F.G., Lima, C.F., Michalewicz, Z. (eds.) Parameter Setting in Evolutionary Algorithms, Springer, Heidelberg (2007)
Dong, H., He, J., Huang, H., Hou, W.: Evolutionary Programming Using a Mixed Mutation Strategy. Information Sciences 177(1), 312–327 (2007)
Lee, C.Y., Yao, X.: Evolutionary programming using the mutations based on on the Lévy probability distribution. IEEE Transactions on Evolutionary Computation 8(1), 1–13 (2004)
Fogel, D.B.: System Identification Through Simulated Evolution: A Machine Learning Approach to Modeling. Ginn Press, Needham Heights, MA 02194 (1991)
Peter, J., Angeline, P.J.: Adaptive and Self-Adaptive Evolutionary Computations. In: Palaniswami, M., Attikiouzel, Y. (eds.) Computational Intelligence: A Dynamic Systems Perspective, IEEE Press, NJ, New York (1995)
Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation 3(2), 82–102 (1999)
Bäck, T., Schwefel, H.-P.: An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation 1(1), 1–23 (1993)
Igel, C., Kreutz, M.: Using Fitness Distributions to Improve the Evolution of Learning Structures. In: Angeline, P.J., Michalewicz, Z., Schoenauer, M., Yao, X., Zalzala, A. (eds.) Proceedings of the Congress on Evolutionary Computation, vol. 3, pp. 1902–1909. IEEE Press, New York (1999)
Igel, C., Chellapilla, K.: Fitness Distributions: Tools for Designing Efficient Evolutionary Computations. In: Spector, L., Langdon, W.B., O’Reilly, U.-M., Angeline, P.J. (eds.) Advances in Genetic Programming 3, ch. 9, pp. 191–216. MIT Press, Cambridge, MA, USA (1999)
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Liu, Y. (2007). Operator Adaptation in Evolutionary Programming. In: Kang, L., Liu, Y., Zeng, S. (eds) Advances in Computation and Intelligence. ISICA 2007. Lecture Notes in Computer Science, vol 4683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74581-5_10
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DOI: https://doi.org/10.1007/978-3-540-74581-5_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74580-8
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