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

Research output: Contribution to journalArticle

56 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 (US)
Pages (from-to)195-221
Number of pages27
JournalJournal of Pharmacokinetics and Pharmacodynamics
Volume33
Issue number2
DOIs
StatePublished - Apr 1 2006

    Fingerprint

Keywords

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

ASJC Scopus subject areas

  • Pharmacology

Cite this