Skip to main content

Genetic Programming Based on Error Decomposition: A Big Data Approach

Part of the Genetic and Evolutionary Computation book series (GEVO)

Abstract

An investigation of the deviations of error and correlation for different stages of the multi-stage genetic programming (MSGP) algorithm in multivariate nonlinear problems is presented. The MSGP algorithm consists of two main stages: (1) incorporating the individual effect of the predictor variables, (2) incorporating the interactions among the predictor variables. The MSGP algorithm formulates these two terms in an efficient procedure to optimize the error among the predicted and the actual values. In addition to this, the proposed pipeline of the MSGP algorithm is implemented with a combination of parallel processing algorithms to run multiple jobs at the same time. To demonstrate the capabilities of the MSGP, its performance is compared with standard GP in modeling a regression problem. The results illustrate that the MSGP algorithm outperforms standard GP in terms of accuracy, efficiency, and computational cost.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-90512-9_9
  • Chapter length: 13 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   84.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-90512-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   109.99
Price excludes VAT (USA)
Hardcover Book
USD   109.99
Price excludes VAT (USA)
Fig. 9.1
Fig. 9.2
Fig. 9.3
Fig. 9.4
Fig. 9.5
Fig. 9.6

References

  1. Brameier, M.F., Banzhaf, W.: Linear genetic programming. Springer Science & Business Media (2007)

    Google Scholar 

  2. Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Niculae, V., Prettenhofer, P., Gramfort, A., Grobler, J., Layton, R., VanderPlas, J., Joly, A., Holt, B., Varoquaux, G.: API design for machine learning software: experiences from the scikit-learn project. In: ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pp. 108–122 (2013)

    Google Scholar 

  3. Fan, J., Han, F., Liu, H.: Challenges of big data analysis. National Science Review 1(2), 293–314 (2014)

    CrossRef  Google Scholar 

  4. Gandomi, A.H., Alavi, A.H.: Multi-stage genetic programming: a new strategy to nonlinear system modeling. Information Sciences 181(23), 5227–5239 (2011)

    CrossRef  Google Scholar 

  5. Gandomi, A.H., Alavi, A.H.: A new multi-gene genetic programming approach to non-linear system modeling. part II: geotechnical and earthquake engineering problems. Neural Computing and Applications 21(1), 189–201 (2012)

    CrossRef  Google Scholar 

  6. Gandomi, A.H., Alavi, A.H.: A new multi-gene genetic programming approach to nonlinear system modeling. part I: materials and structural engineering problems. Neural Computing and Applications 21(1), 171–187 (2012)

    CrossRef  Google Scholar 

  7. Gandomi, A.H., Alavi, A.H., Mirzahosseini, M.R., Nejad, F.M.: Nonlinear genetic-based models for prediction of flow number of asphalt mixtures. Journal of Materials in Civil Engineering 23(3), 248–263 (2010)

    CrossRef  Google Scholar 

  8. Gandomi, A.H., Roke, D.A.: Assessment of artificial neural network and genetic programming as predictive tools. Advances in Engineering Software 88, 63–72 (2015)

    CrossRef  Google Scholar 

  9. Gandomi, A.H., Sajedi, S., Kiani, B., Huang, Q.: Genetic programming for experimental big data mining: A case study on concrete creep formulation. Automation in Construction 70, 89–97 (2016)

    CrossRef  Google Scholar 

  10. Garzón-Roca, J., Marco, C.O., Adam, J.M.: Compressive strength of masonry made of clay bricks and cement mortar: Estimation based on neural networks and fuzzy logic. Engineering Structures 48, 21–27 (2013)

    CrossRef  Google Scholar 

  11. Iba, H., deGaris, H., Sato, T.: A numerical approach to genetic programming for system identification. Evolutionary Computation 3(4), 417–452 (1995).

    CrossRef  Google Scholar 

  12. Koza, J.R.: Genetic programming: on the programming of computers by means of natural selection, vol. 1. MIT Press (1992)

    Google Scholar 

  13. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011)

    Google Scholar 

  14. Poli, R., Langdon, W.B., McPhee, N.F., Koza, J.R.: A field guide to genetic programming. Lulu. com (2008)

    Google Scholar 

  15. Ryan, C., Collins, J., Neill, M.: Grammatical evolution: Evolving programs for an arbitrary language. In: European Conference on Genetic Programming, Paris 1998, pp. 83–96 (1998) Springer, Berlin (1998)

    Google Scholar 

  16. Schadt, E.E., Linderman, M.D., Sorenson, J., Lee, L., Nolan, G.P.: Cloud and heterogeneous computing solutions exist today for the emerging big data problems in biology. Nature Reviews Genetics 12(3), 224–224 (2011)

    CrossRef  Google Scholar 

  17. Smith, G.N.: Probability and statistics in civil engineering. Collins Professional and Technical Books 244 (1986)

    Google Scholar 

  18. Tahmassebi, A., Gandomi, A.H.: Building energy consumption forecast using multi-objective genetic programming. Measurement 118, 164–171 (2018)

    CrossRef  Google Scholar 

  19. Tahmassebi, A., Gandomi, A.H., McCann, I., Schulte, M.H., Schmaal, L., Goudriaan, A.E., Meyer-Bäse, A.: An evolutionary approach for fMRI big data classification. In: 2017 IEEE Congress on Evolutionary Computation (CEC) pp. 1029–1036 (2017)

    Google Scholar 

  20. Tahmassebi, A., Gandomi, A.H., Meyer-Bäse, A.: High performance GP-based approach for fMRI big data classification. In: Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact, PEARC17, pp. 57:157:4. ACM Press, New York, NY, USA (2017)

    Google Scholar 

  21. Wu, X., Zhu, X., Wu, G.Q., Ding, W.: Data mining with big data. IEEE transactions on knowledge and data engineering 26(1), 97–107 (2014)

    CrossRef  Google Scholar 

  22. Zhang, B.T., Mühlenbein, H.: Balancing accuracy and parsimony in genetic programming. Evolutionary Computation 3(1), 17–38 (1995)

    CrossRef  Google Scholar 

Download references

Acknowledgements

This material is based in part upon work supported by the National Science Foundation under Cooperative Agreement No. DBI-0939454. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. For valuable help in the revision of the chapter we also would like to thank Eitan Lees for critical and helpful comments on final draft.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Amir H. Gandomi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Tahmassebi, A., Gandomi, A.H. (2018). Genetic Programming Based on Error Decomposition: A Big Data Approach. In: Banzhaf, W., Olson, R., Tozier, W., Riolo, R. (eds) Genetic Programming Theory and Practice XV. Genetic and Evolutionary Computation. Springer, Cham. https://doi.org/10.1007/978-3-319-90512-9_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-90512-9_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-90511-2

  • Online ISBN: 978-3-319-90512-9

  • eBook Packages: Computer ScienceComputer Science (R0)