ICONIP 2013: Neural Information Processing pp 51-58 | Cite as
Genetic Network Programming with Simplified Genetic Operators
Abstract
Recently, a novel type of evolutionary algorithms (EAs), called Genetic Network Programming (GNP), has been proposed. Inspired by the complex human brain structures, GNP develops a distinguished directed graph structure for its individual representations, consequently showing an excellent expressive ability for modelling a range of complex problems. This paper is dedicated to reveal GNP’s unique features. Accordingly, simplified genetic operators are proposed to highlight such features of GNP, reduce its computational effort and provide better results. Experimental results are presented to confirm its effectiveness over original GNP and several state-of-the-art algorithms.
Keywords
evolutionary algorithms genetic network programming directed graph transition by necessity invalid evolutionPreview
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References
- 1.Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)CrossRefGoogle Scholar
- 2.Teller, A., Veloso, M.: PADO: Learning tree structured algorithms for orchestration into an object recognition system. Tech. Report CMU-CS-95-101, Carnegie Mellon University (1995)Google Scholar
- 3.Hirasawa, K., Okubo, M., Katagiri, H., Hu, J., Murata, J.: Comparison between genetic network programming (GNP) and genetic programming (GP). In: Proc. of the IEEE Congress on Evol. Comput., pp. 1276–1282 (2001)Google Scholar
- 4.Mabu, S., Hirasawa, K., Hu, J.: A graph-based evolutionary algorithm: Genetic network programming (GNP) and its extension using reinforcement learning. Evol. Comput. 15, 369–398 (2007)CrossRefGoogle Scholar
- 5.Li, X., Mabu, S., Hirasawa, K.: A novel graph-based estimation of distribution algorithm and its extension using reinforcement learning. IEEE Trans. Evol. Comput. (early access, 2013)Google Scholar
- 6.Hirasawa, K., Eguchi, T., Zhou, J., Yu, L., Markon, S.: A double-deck elevator group supervisory control system using genetic network programming. IEEE Trans. Syst., Man, Cybern. C 38, 535–550 (2008)CrossRefGoogle Scholar
- 7.Li, X., Mabu, S., Zhou, H., Shimada, K., Hirasawa, K.: Genetic network programming with estimation of distribution algorithms for class association rule mining in traffic prediction. In: Proc. of the IEEE Congress on Evol. Comput., pp. 2673–2680 (2010)Google Scholar
- 8.Li, X., Mabu, S., Hirasawa, K.: An extended probabilistic model building genetic network programming using both of good and bad individuals. IEEJ Trans. on Electrical and Electronic Engineering 8, 339–347 (2013)CrossRefGoogle Scholar
- 9.Li, X., Hirasawa, K.: Extended rule-based genetic network programming. In: Proc. of the Genetic and Evol. Comput. Conf. Companion, pp. 155–156 (2013)Google Scholar
- 10.Pollack, M.E., Ringuette, M.: Introducing the tile-world: Experimentally evaluating agent architectures. In: Proc. of the AAAI, pp. 183–189 (1990)Google Scholar
- 11.Hirasawa, K., Ohbayashi, M., Sakai, S., Hu, J.: Learning Petri network and its application to nonlinear system control. IEEE Trans. Syst., Man, Cybern. B 28(6), 781–789 (1998)CrossRefGoogle Scholar
- 12.Katagiri, H., Hirasawa, K., Hu, J., Murata, J.: Comparing some graph crossover in genetic network programming. In: Proc. of the SICE Conf., pp. 1263–1268 (2002)Google Scholar
- 13.Collin, R., Cipriani, R.: Dollo’s law and the re-evolution of shell coiling. Royal Society B 270(1533), 2551–2555 (2003)CrossRefGoogle Scholar
- 14.Koza, J.R.: Genetic Programming, on the Programming of Computers by Means of Natural Selection. MIT Press (1992)Google Scholar
- 15.Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press (1998)Google Scholar