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A genetic algorithm-based, hybrid machine learning approach to model selection

  • Robert R. Bies
  • , Matthew F. Muldoon
  • , Bruce G. Pollock
  • , Steven Manuck
  • , Gwenn Smith
  • , Mark E. Sale
  • University of Pittsburgh
  • University of Toronto
  • Albert Einstein College of Medicine
  • Next Level Solutions

Research output: Contribution to journalArticlepeer-review

117 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)195-221
Number of pages27
JournalJournal of Pharmacokinetics and Pharmacodynamics
Volume33
Issue number2
DOIs
StatePublished - Apr 2006

Keywords

  • Automated machine learning
  • Covariate selection
  • Genetic algorithm
  • Model building
  • Nonlinear mixed effects modeling
  • Population paramacokinetics

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