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Genetic Programming Based on Error Decomposition: A Big Data Approach

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Genetic Programming Theory and Practice XV

Part of the book series: Genetic and Evolutionary Computation ((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.

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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

  1. Department of Scientific Computing, Florida State University, Tallahassee, FL, USA

    Amirhessam Tahmassebi

  2. School of Business, Stevens Institute of Technology, Hoboken, NJ, USA

    Amir H. Gandomi

  3. BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI, USA

    Amir H. Gandomi

Authors
  1. Amirhessam Tahmassebi
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  2. Amir H. Gandomi
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Correspondence to Amir H. Gandomi .

Editor information

Editors and Affiliations

  1. BEACON Center for the Study of Evolution in Action and Department of Computer Science, Michigan State University, East Lansing, Michigan, USA

    Wolfgang Banzhaf

  2. Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Randal S. Olson

  3. Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA

    William Tozier

  4. Center for the Study of Complex Systems, University of Michigan, Ann Arbor, Michigan, USA

    Rick Riolo

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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

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

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-90511-2

  • Online ISBN: 978-3-319-90512-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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