Gene Expression Programming Neural Network for Regression and Classification (original) (raw)

Abstract

Gene Expression Programming(GEP) is a kind of heuristic method based on evolutionary computation theory. Basic GEP method has been proved to be powerful in symbolic regression and data mining tasks. However, GEP’s potential for neural network learning has not been well studied. In this paper, we prove that basic GEP neural network(GEPNN) is unable to solve difficult regression and classification problems. Based on our proof, we propose an extended method for evolving neural network with GEP. The extended GEPNN is used in function finding and classification problems. Results on multiple learning methods show the effectiveness of our method.

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References

  1. Ferreira, C.: Gene Expression Programming: A New Adaptive Algorithm for Solving Problems. Complex Systems (2001)
    Google Scholar
  2. Ferreira, C.: Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence, 2nd edn. Springer, Germany (2006)
    MATH Google Scholar
  3. Zhou, C., Xiao, W., Nelson, P.C., Tirpak, T.M.: Evolving Accurate and Compact Classification Rules with Gene Expression Programming. IEEE Transactions on Evolutionary Computation 7(6), 519–531 (2003)
    Article Google Scholar
  4. Haykin, S.: Neural Networks: A Comprehensive Foundation. IEEE Society Press, Macmillan College Publishing (1992)
    Google Scholar
  5. Ritchie, M.D., Coffey, C.S., Moore, J.H.: Genetic Programming Neural Networks as a Bioinformatics Tool for Human Genetics. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 438–448. Springer, Heidelberg (2004)
    Chapter Google Scholar
  6. Yao, X.: Evolving artificial neural networks. Proc. IEEE 87(9), 1423–1447 (1999)
    Article Google Scholar
  7. Blake, C., Keogh, E., Merz, C.J.: UCI Repository of Machine Learning Databases. Univ. California at Irvine, Dept. Inform. Comput. Sci., CA (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
  8. Witten, I.H., Frank, E.: Data Mining Practical Machine Learning Tools and Techniques with java Implementations. Morgan Kaufmann Publishers, USA (2000)
    Google Scholar

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Authors and Affiliations

  1. Software College, Zhejiang University of Technology, Hangzhou, China, 310032
    Weihong Wang, Qu Li & Xing Qi

Authors

  1. Weihong Wang
  2. Qu Li
  3. Xing Qi

Editor information

Editors and Affiliations

  1. Computation Center, Wuhan University, 430072, Wuhan, China
    Lishan Kang
  2. Faculty of Computer Science, China University of Geosciences, 430074, Wuhan, Hubei, P.R. China
    Zhihua Cai
  3. School of Computer Science, China University of Geosciences, Wu-Han, 430074, China Research Center for Space Science and Technology, China University of Geosciences, 430074, Wu-Han, China
    Xuesong Yan
  4. The University of Aizu, Tsuruga, Ikki-machi, 965-8580, Aizu-Wakamatsu City Fukushima, Japan
    Yong Liu

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© 2008 Springer-Verlag Berlin Heidelberg

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Wang, W., Li, Q., Qi, X. (2008). Gene Expression Programming Neural Network for Regression and Classification. In: Kang, L., Cai, Z., Yan, X., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2008. Lecture Notes in Computer Science, vol 5370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92137-0\_24

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