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Evaluation of derivative-free optimizers for parameter estimation in systems biology.

IFAC PapersOnline 51, 98-101 (2018)
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Open Access Gold
Derivative-free optimization can be used to estimate parameters without computing derivatives. As there exist many methods, it is unclear which to use in practice. Here, we present two comparative studies of 18 state-of-the-art methods: Firstly, we evaluate them on a set of 466 classic optimization test problems of dimension 2 to 300. Secondly, we study their performance in parameter estimation on 8 ODE models of biological processes, comparing them to state-of-the-art derivative-based optimization. We observe that different problem features necessitate the use of different methods, for which we can give suggestions based on our findings. Our analysis suggests that classic test problems are not representative for problems in systems biology. For ODE models, we find that purely derivative-free methods are for most problems not reliable or at least inferior to derivative-based methods.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Derivative-free Optimization ; Ode Models ; Parameter Estimation
Language english
Publication Year 2018
HGF-reported in Year 2018
ISSN (print) / ISBN 2405-8963
e-ISSN 1474-6670
Quellenangaben Volume: 51, Issue: 19, Pages: 98-101 Article Number: , Supplement: ,
Publisher Elsevier
Publishing Place Frankfurt ; München [u.a.]
Reviewing status Peer reviewed
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-553800-001
Scopus ID 85054404401
Erfassungsdatum 2018-10-23