Abstract
The mutation operator is the only source of variation in Evolutionary Programming. In the past these have been human nominated and included the Gaussian, Cauchy, and the Lévy distributions. We automatically design mutation operators (probability distributions) using Genetic Programming. This is done by using a standard Gaussian random number generator as the terminal set and and basic arithmetic operators as the function set. In other words, an arbitrary random number generator is a function of a randomly (Gaussian) generated number passed through an arbitrary function generated by Genetic Programming.
Rather than engaging in the futile attempt to develop mutation operators for arbitrary benchmark functions (which is a consequence of the No Free Lunch theorems), we consider tailoring mutation operators for particular function classes. We draw functions from a function class (a probability distribution over a set of functions). The mutation probability distribution is trained on a set of function instances drawn from a given function class. It is then tested on a separate independent test set of function instances to confirm that the evolved probability distribution has indeed generalized to the function class.
Initial results are highly encouraging: on each of the ten function classes the probability distributions generated using Genetic Programming outperform both the Gaussian and Cauchy distributions.
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References
Back, T., Schwefel, H.P.: An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation 1, 1–23 (1993)
Burke, E.K., Hyde, M.R., Kendall, G., Ochoa, G., Ozcan, E., Woodward, J.R.: Exploring Hyper-heuristic Methodologies with Genetic Programming. In: Mumford, C.L., Jain, L.C. (eds.) Computational Intelligence. ISRL, vol. 1, pp. 177–201. Springer, Heidelberg (2009)
Dong, H., He, J., Huang, H., Hou, W.: Evolutionary programming using a mixed mutation strategy. Information Science, 312–327 (2007)
Fogel, D.B.: Evolving artificial intelligence. PhD thesis, University of California, San Diego (1992)
Lee, C.Y., Yao, X.: Evolutionary programming using mutations based on the lévy probability distribution. IEEE Transactions on Evolutionary Computation 8 (2004)
Mallipeddi, R., Suganthan, P.N.: Evaluation of novel adaptive evolutionary programming on four constraint handling techniques. In: IEEE Congress on Evolutionary Computation, pp. 4045–4052 (2008)
Mallipeddi, R., Mallipeddi, S., Suganthan, P.N.: Ensemble strategies with adaptive evolutionary programming. Information Science, 1571–1581 (2010)
Poli, R., Langdon, W.B., et al.: A field guide to genetic programming (2008) ISBN 978-1-4092-0073-4
Su Nguyen, M.Z., Johnston, M.: A genetic programming based hyper-heuristic approach for combinatorial optimization. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, pp. 1299–1306 (2011) ISBN 978-1-4503-0557-0
Woodward, J.: The necessity of meta bias in search algorithms. In: IEEE International Conference on Computational Intelligence and Software Engineering, CiSE (2010)
Woodward, J., Swan, J.: Automatically designing selection heuristics. In: ACM Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 583–590 (2011)
Woodward, J., Swan, J.: The automatic generation of mutation operators for genetic algorithms. In: ACM Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference Companion, pp. 67–74 (2012)
Xin Yao, Y.L., Lin, G.: Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation 3, 82–102 (1999)
Yao, X., Liu, Y.: Fast evolutionary programming. In: Proceedings of the Fifth Annual Conference on Evolutionary Programming, pp. 451–460. MIT Press (1996)
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Hong, L., Woodward, J., Li, J., Özcan, E. (2013). Automated Design of Probability Distributions as Mutation Operators for Evolutionary Programming Using Genetic Programming. In: Krawiec, K., Moraglio, A., Hu, T., Etaner-Uyar, A.Ş., Hu, B. (eds) Genetic Programming. EuroGP 2013. Lecture Notes in Computer Science, vol 7831. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37207-0_8
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DOI: https://doi.org/10.1007/978-3-642-37207-0_8
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