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

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Genetic Programming Theory and Practice XI

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

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Abstract

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.

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Acknowledgements

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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Authors and Affiliations

  1. Department of Computer Science, University of Vermont & Vermont Complex Systems Center, Burlington, VT, USA

    Ilknur Icke & Joshua C. Bongard

  2. Department of Mathematics and Statistics, University of Vermont & Vermont Complex Systems Center, Burlington, VT, USA

    Nicholas A. Allgaier & Christopher M. Danforth

  3. Department of Psychiatry, University of Vermont, Burlington, VT, USA

    Robert A. Whelan & Hugh P. Garavan

Authors
  1. Ilknur Icke
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  2. Nicholas A. Allgaier
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  3. Christopher M. Danforth
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  4. Robert A. Whelan
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  5. Hugh P. Garavan
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  6. Joshua C. Bongard
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Corresponding author

Correspondence to Ilknur Icke .

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Editors and Affiliations

  1. University of Michigan, Ann Arbor, Michigan, USA

    Rick Riolo

  2. Inst for Quantitative Biomedical Science, Dartmouth Medical School, Lebanon, New Hampshire, USA

    Jason H. Moore

  3. Evolved Analytics, Midland, Michigan, USA

    Mark Kotanchek

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Icke, I., Allgaier, N.A., Danforth, C.M., Whelan, R.A., Garavan, H.P., Bongard, J.C. (2014). A Deterministic and Symbolic Regression Hybrid Applied to Resting-State fMRI Data. In: Riolo, R., Moore, J., Kotanchek, M. (eds) Genetic Programming Theory and Practice XI. Genetic and Evolutionary Computation. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0375-7_9

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  • DOI: https://doi.org/10.1007/978-1-4939-0375-7_9

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