Skip to main content
Springer Nature Link
Log in

The Recent Developments and Comparative Analysis of Neural Network and Evolutionary Algorithms for Solving Symbolic Regression

  • Conference paper
  • First Online:
Intelligent Computing Theories and Methodologies (ICIC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9225))

Included in the following conference series:

Abstract

Symbolic regression (SR) is one of the research fields in data mining, how to use scientific and appropriate methods to study SR is a difficult problem. The traditional methods used in SR mainly focus on the models such as genetic programming (GP), the article applies the gene expression programming (GEP) and neural network (NN) to this field, in order to correctly compare the advantages and disadvantages of the three methods, some relevant works have been done. This paper first briefly introduces the NN and evolutionary algorithms including GP and GEP, their design steps and recent developments, and applies these algorithms to SR, then uses the algorithms to solve SR and makes comparison analysis, and draws some conclusions in the experiment condition: the performance of NN and evolutionary algorithms change dramatically for solving this problem; GP and GEP fluctuate greatly compared with NN, the used time is also less, and NN shows better stability and result.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Zeng, T., Tang, C., et al.: Mining multi-dimensional complex association rule based on artificial immune system and gene expression programming. J. Sichuan Univ. (Eng. Sci. Ed.) 38(5), 136–142 (2006)

    MathSciNet  Google Scholar 

  2. Wang, X., Cao, L., Gu, S.: Genetic programming and its applications in the symbolic regression. J. Tongji Univ. 29(10), 1200–1203 (2001)

    Google Scholar 

  3. Chen, Y., Yang, J., Yang, J., et al.: Research and application of genetic expression programming algorithm based on uniform-design. Comput. Appl. 27(4), 948–951 (2007)

    Google Scholar 

  4. Lu, X., Cai, Z.: Application of a novel GEP algorithm in evolutionary modeling and forecasting. Comput. Appl. 25(12), 2784–2787 (2005)

    Google Scholar 

  5. Fang, W., Zhang, K., Shao, L.: Complex function modeling based on improved gene expression programming. Comput. Eng. 32(21), 188–190 (2006)

    Google Scholar 

  6. Peng, J., Tang, C., Yuan, C., et al.: A multi-gene evolutionary algorithm based on overlapped expression. Chin. J. Comput. 30(5), 775–785 (2007)

    MathSciNet  Google Scholar 

  7. Luo, P., Wang, X., Tang, X.: Hierarchical face parsing via deep learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2480–2487 (2012)

    Google Scholar 

  8. Baylar, A., Unsal, M., Ozkan, F.: GEP modeling of oxygen transfer efficiency prediction in aeration cascades. KSCE J. Civ. Eng. 15(5), 799–804 (2011)

    Article  MATH  Google Scholar 

  9. Xia, Y., Tian, S., Wei, H., Wang, Z.: Research on symbolic regression based on genetic programming. J. China Jiliang Univ. 17(2), 128–131 (2006)

    Google Scholar 

  10. Deng, W., Zheng, Q., et al.: Research on extreme learning of neural networks. Chin. J. Comput. 33(2), 279–286 (2010)

    Article  MathSciNet  Google Scholar 

  11. Guo, T., Zhang, X., Liang, Z.: Research on change information recognition method of vector data based on neural network decision tree. Acta Geodaet. Cartographica Sin. 42(6), 937–943 (2013)

    Google Scholar 

  12. Zhang, X., He, G.: The forecasting approach for short-term traffic flow based on principal component analysis and combined NN. Syst. Eng. Theor. Pract. 8, 168–170 (2007)

    Google Scholar 

  13. Li, A., Luo, S., Huang, H., et al.: Decision tree based neural network design. J. Comput. Res. Devel. 42(8), 1312–1317 (2005)

    Article  Google Scholar 

  14. Deng, S., Wang, R., Zhang, Y., Zhang, J.: Grid resource allocation algorithm based on parallel gene expression programming. Acta Electronica Sin. 37(2), 272–277 (2009)

    Google Scholar 

  15. Peng, J., Tang, C., Li, C., Hu, J.: M2GEP: a new evolution algorithm based on multi2layer chromosomes gene expression programming. Chin. J. Comput. 28(9), 1459–1466 (2005)

    Google Scholar 

  16. Hu, J., Tang, C., Duan, L., et al.: The strategy for diversifying initial population of gene expression programming. Chin. J. Comput. 30(2), 305–310 (2007)

    Google Scholar 

  17. Li, B., Dong, J., Liu, Y., Mi, S.: Fuzzy lattice constructive morphological neural network. Acta Electronica Sin. 42(2), 319–326 (2014)

    Google Scholar 

  18. Sun, Y., Wang, X., Tang, X.: Deep convolutional network cascade for facial point detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3476–3483 (2013)

    Google Scholar 

  19. Farabet, C., Couprie, C., Najman, L., LeCun, Y.: Learning hierarchical features for scene labeling. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1915–1928 (2013)

    Article  Google Scholar 

  20. Ouyang, W., Wang, X.: A discriminative deep model for pedestrian detection with occlusion handling. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3258–3265 (2012)

    Google Scholar 

  21. Shang, C., Yang, F., Huang, D., Lya, W.: Data-driven soft sensor development based on deep learning technique. J. Process Control 24, 223–233 (2014)

    Article  Google Scholar 

  22. Zhang, G., Wang, W.: Application introduction of uniform experiment design method. Appl. Stat. Manage. 32(1), 89–98 (2013)

    Google Scholar 

  23. Karaboga, D., Ozturk, C., Karaboga, N., Gorkemli, B.: Artificial bee colony programming for symbolic regression. Inf. Sci. 209, 1–15 (2012)

    Article  Google Scholar 

  24. Chen, J., Zeng, Z., Jiang, P.: On the periodic dynamics of memristor-based neural networks with time-varying delays. Inf. Sci. 279, 358–373 (2014)

    Article  MathSciNet  Google Scholar 

  25. Melin, P., Amezcua, J., Valdez, F., Castillo, O.: A new neural network model based on the LVQ algorithm for multi-class classification of arrhythmias. Inf. Sci. 279, 483–497 (2014)

    Article  MathSciNet  Google Scholar 

  26. Pulido, M., Melin, P., Castillo, O.: Particle swarm optimization of ensemble neural networks with fuzzy aggregation for time series prediction of the Mexican Stock Exchange. Inf. Sci. 280, 188–204 (2014)

    Article  MathSciNet  Google Scholar 

  27. Gaxiola, F., Melin, P., Valdez, F., Castillo, O.: Interval type-2 fuzzy weight adjustment for backpropagation neural networks with application in time series prediction. Inf. Sci. 260, 1–14 (2014)

    Article  MathSciNet  Google Scholar 

  28. Peng, Yu.: New application of symbolic regression method based on genetic programming in power quality analysis. Electron. Des. Eng. 21(7), 20–23 (2013)

    MATH  Google Scholar 

Download references

Acknowledgement

We are gratefully acknowledged the financial support from the National Natural Science Foundation of China under Grant No. 61170305, No. 60873114 and the Program of Scientifc Research and Technology in Liuzhou No. 2014J020401. The authors would like to acknowledge Mr. Andrew Kirillov for providing the codes mentioned above in the paper.

Author information

Authors and Affiliations

  1. Computer School, Wuhan University, Wuhan, 430072, Hubei, China

    Xueshi Dong, Wenyong Dong, Yunfei Yi, Yajie Wang & Xiaosong Xu

  2. College of Computer and Information Science, Hechi University, Yizhou, 546300, Guangxi, China

    Yunfei Yi

  3. Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Nanning, 530006, Guangxi, China

    Yunfei Yi

Authors
  1. Xueshi Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenyong Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yunfei Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yajie Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaosong Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wenyong Dong .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Dong, X., Dong, W., Yi, Y., Wang, Y., Xu, X. (2015). The Recent Developments and Comparative Analysis of Neural Network and Evolutionary Algorithms for Solving Symbolic Regression. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_70

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-22180-9_70

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22179-3

  • Online ISBN: 978-3-319-22180-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics