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 › peer-review

63 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 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
State	Published - Apr 1 2006
Externally published	Yes

Keywords

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

ASJC Scopus subject areas

Pharmacology

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10.1007/s10928-006-9004-6

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AU - Muldoon, Matthew F.

AU - Pollock, Bruce G.

AU - Manuck, Steven

AU - Smith, Gwenn

AU - Sale, Mark E.

PY - 2006/4/1

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KW - Covariate selection

KW - Genetic algorithm

KW - Model building

KW - Nonlinear mixed effects modeling

KW - Population paramacokinetics

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

Abstract

Keywords

ASJC Scopus subject areas

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