A Deterministic and Symbolic Regression Hybrid Applied to Resting-State fMRI Data

  • Ilknur IckeEmail author
  • Nicholas A. Allgaier
  • Christopher M. Danforth
  • Robert A. Whelan
  • Hugh P. Garavan
  • Joshua C. Bongard
Part of the Genetic and Evolutionary Computation book series (GEVO)


Symbolic regression (SR) is one the most popular applications of genetic programming (GP) and an attractive alternative to the standard deterministic regression approaches due to its flexibility in generating free-form mathematical models from observed data without any domain knowledge. However, GP suffers from various issues hindering the applicability of the technique to real-life problems. In this paper, we show that a hybrid deterministic regression (DR)/genetic programming based symbolic regression (GP-SR) algorithm outperforms GP-SR alone on a brain imaging dataset.


Symbolic regression Hybrid algorithm Regularization Resting-state fMRI 



This research was partially supported by a DARPA grant #FA8650-11-1-7155. We thank the IMAGEN consortium for providing us with the resting-state fMRI dataset. The authors also acknowledge the Vermont Advanced Computing Core which is supported by NASA (NNX 06AC88G), at the University of Vermont for providing High Performance Computing resources that have contributed to the research results reported within this paper.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Ilknur Icke
    • 1
    Email author
  • Nicholas A. Allgaier
    • 2
  • Christopher M. Danforth
    • 2
  • Robert A. Whelan
    • 3
  • Hugh P. Garavan
    • 3
  • Joshua C. Bongard
    • 1
  1. 1.Department of Computer ScienceUniversity of Vermont & Vermont Complex Systems CenterBurlingtonUSA
  2. 2.Department of Mathematics and StatisticsUniversity of Vermont & Vermont Complex Systems CenterBurlingtonUSA
  3. 3.Department of PsychiatryUniversity of VermontBurlingtonUSA

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