Advertisement

A Deep Learning Assisted Gene Expression Programming Framework for Symbolic Regression Problems

  • Jinghui ZhongEmail author
  • Yusen Lin
  • Chengyu Lu
  • Zhixing Huang
Conference paper
  • 1.7k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11307)

Abstract

Genetic programming is a powerful evolutionary algorithm that solves user-defined tasks through the evolution of computer programs. Selecting a proper set of function primitives is a fundamental and challenging operation in applying GP to real applications. Traditional manual design methods require a lot of domain knowledge and are not effective and convenient enough. To address this issue, this paper proposed an automatic function primitive identification mechanism. The key idea is to train a deep convolutional neural network to predict the probability of the existence of a function primitive in the target solution. During the evolution of GP, function primitives with higher probabilities are more likely to be selected to construct solutions. The proposed method is tested on nine benchmark problems and the experimental results have demonstrated the efficacy of the proposed method.

Keywords

Genetic Programming (GP) Deep Learning (DL) Convolutional Neural Network (CNN) 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 61602181), the Fundamental Research Funds for the Central Universities (Grant No. 2017ZD053), the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (Grant No. 2017ZT07X183), and the Guangzhou Science and Technology Plan Project (Grant No. 201804010245).

References

  1. 1.
    Berry, M.J., Linoff, G.S.: Data Mining Techniques. Wiley, Hoboken (2009)Google Scholar
  2. 2.
    Brameier, M.F., Banzhaf, W.: Linear Genetic Programming. Springer, Boston (2007).  https://doi.org/10.1007/978-0-387-31030-5CrossRefzbMATHGoogle Scholar
  3. 3.
    Castelli, M., Vanneschi, L., Silva, S.: Semantic search-based genetic programming and the effect of intron deletion. IEEE Trans. Cybern. 404(1), 103–113 (2014).  https://doi.org/10.1109/TSMCC.2013.2247754CrossRefGoogle Scholar
  4. 4.
    Cramer, N.L.: A representation for the adaptive generation of simple sequential programs. In: Proceedings of the 1st International Conference on Genetic Algorithms, pp. 183–187. L. Erlbaum Associates Inc., Hillsdale (1985)Google Scholar
  5. 5.
    Ferreira, C.: Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst. 13(2), 8–129 (2001)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Ferreira, C.: Gene Expression Programming. Springer, Berlin (2006).  https://doi.org/10.1007/3-540-32849-1CrossRefzbMATHGoogle Scholar
  7. 7.
    Ffrancon, R., Schoenauer, M.: Memetic semantic genetic programming. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 1023–1030. ACM (2015)Google Scholar
  8. 8.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  9. 9.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection, vol. 1. MIT press, Cambridge (1992)zbMATHGoogle Scholar
  10. 10.
    Lamos-Sweeney, J.D.: Deep learning using genetic algorithms. Dissertations and Theses - Gradworks (2012)Google Scholar
  11. 11.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)CrossRefGoogle Scholar
  12. 12.
    Mahsal Khan, M., Khan, G.M., Miller, J.: Evolution of optimal ANNs for non-linear control problems using cartesian genetic programming, vol. 1, pp. 339–346 (2010)Google Scholar
  13. 13.
    Miller, J.F., Thomson, P.: Cartesian genetic programming. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds.) EuroGP 2000. LNCS, vol. 1802, pp. 121–132. Springer, Heidelberg (2000).  https://doi.org/10.1007/978-3-540-46239-2_9CrossRefGoogle Scholar
  14. 14.
    Moraglio, A., Krawiec, K., Johnson, C.G.: Geometric semantic genetic programming. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012. LNCS, vol. 7491, pp. 21–31. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-32937-1_3CrossRefGoogle Scholar
  15. 15.
    O’Neill, M., Ryan, C.: Grammatical evolution. IEEE Trans. Evol. Comput. 5(4), 349–358 (2001)CrossRefGoogle Scholar
  16. 16.
    Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)Google Scholar
  17. 17.
    Turner, A.J., Miller, J.F.: Recurrent cartesian genetic programming of artificial neural networks. Genet. Program. Evolvable Mach. 18(2), 185–212 (2017)CrossRefGoogle Scholar
  18. 18.
    Yao, X.: Evolving artificial neural networks. Proc. IEEE 87(9), 1423–1447 (1999)CrossRefGoogle Scholar
  19. 19.
    Zhong, J., Feng, L., Cai, W., Ong, Y.: Multifactorial genetic programming for symbolic regression problems. IEEE Trans. Syst. Man Cybern. Syst. (2018, in press).  https://doi.org/10.1109/TSMC.2018.2853719
  20. 20.
    Zhong, J., Cai, W., Lees, M., Luo, L.: Automatic model construction for the behavior of human crowds. Appl. Soft Comput. 56, 368–378 (2017)CrossRefGoogle Scholar
  21. 21.
    Zhong, J., Feng, L., Ong, Y.S.: Gene expression programming: a survey. IEEE Comput. Intell. Mag. 12(3), 54–72 (2017)CrossRefGoogle Scholar
  22. 22.
    Zhong, J., Ong, Y.S., Cai, W.: Self-learning gene expression programming. IEEE Trans. Evol. Comput. 20(1), 65–80 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Jinghui Zhong
    • 1
    Email author
  • Yusen Lin
    • 1
  • Chengyu Lu
    • 1
  • Zhixing Huang
    • 1
  1. 1.Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information, School of Computer Science and EngineeringSouth China University of TechnologyGuangzhouChina

Personalised recommendations