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Genetic Programming Based on Error Decomposition: A Big Data Approach

  • Amirhessam Tahmassebi
  • Amir H. Gandomi
Conference paper
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.

Notes

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.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Scientific ComputingFlorida State UniversityTallahasseeUSA
  2. 2.School of BusinessStevens Institute of TechnologyHobokenUSA
  3. 3.BEACON Center for the Study of Evolution in ActionMichigan State UniversityEast LansingUSA

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