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A Genetic Algorithm-Based, Hybrid Machine Learning Approach to Model Selection

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We describe a general and robust method for identification of an optimal non-linear mixed effects model. This includes structural, inter-individual random effects, covariate effects and residual error models using machine learning. This method is based on combinatorial optimization using genetic algorithm.

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Authors and Affiliations

  1. Department of Pharmaceutical Sciences and Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA

    Robert R. Bies

  2. Center for Clinical Pharmacology, University of Pittsburgh, Pittsburgh, PA, USA

    Matthew F. Muldoon

  3. Rotman Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada

    Bruce G. Pollock

  4. Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA

    Steven Manuck

  5. Albert Einstein College of Medicine, The Zucker Hillside Hospital, Glen Oaks, NY, USA

    Gwenn Smith

  6. Next Level Solutions, Raleigh, NC, USA

    Mark E. Sale

Authors
  1. Robert R. Bies
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  2. Matthew F. Muldoon
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  3. Bruce G. Pollock
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  4. Steven Manuck
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  5. Gwenn Smith
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  6. Mark E. Sale
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Corresponding author

Correspondence to Robert R. Bies.

Additional information

GRANT SUPPORT: NIBIB P41 EB001975, NIMH PO1 HL40962, MH064823.

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Bies, R.R., Muldoon, M.F., Pollock, B.G. et al. A Genetic Algorithm-Based, Hybrid Machine Learning Approach to Model Selection. J Pharmacokinet Pharmacodyn 33, 195–221 (2006). https://doi.org/10.1007/s10928-006-9004-6

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  • DOI: https://doi.org/10.1007/s10928-006-9004-6

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