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

doi:10.1007/s10928-006-9004-6

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 journal › Article

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 language	English (US)
Pages (from-to)	195-221
Number of pages	27
Journal	Journal of Pharmacokinetics and Pharmacodynamics
Volume	33
Issue number	2
DOIs	https://doi.org/10.1007/s10928-006-9004-6
Publication status	Published - Apr 2006
Externally published	Yes

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Keywords

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

ASJC Scopus subject areas

Pharmacology
Catalysis

Cite this

Bies, R. R., Muldoon, M. F., Pollock, B. G., Manuck, S., Smith, G., & Sale, M. E. (2006). A genetic algorithm-based, hybrid machine learning approach to model selection. Journal of Pharmacokinetics and Pharmacodynamics, 33(2), 195-221. https://doi.org/10.1007/s10928-006-9004-6

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10.1007/s10928-006-9004-6