Evaluation of Derivative-Free Optimizers for Parameter Estimation in Systems Biology

Revision as of 14:39, 25 February 2020 by Bwday (talk | contribs) (Study outcomes)

1 Citation

Evaluation of Derivative-Free Optimizers for Parameter Estimation in Systems Biology, Y Schälte, P Stapor, J Hasenauer, IFAC-PapersOnLine 51 (19), 98-101.

2 Summary

Different parameter estimation settings necessitate the use of different optimization techniques. Hence, different local and global optimization techniques were compared in this paper by use of classic optimization test problems and also 8 ODE models. This article especially focused on the performance of derivative free optimizations (DFO) and if they are a valuable alternative to gradient-based methods.

3 Study outcomes

This section purely focuses on the outcomes obtained for ODE-models

3.1 ODE models are complicated

The results on the test models are not representative for the behavior of the optimization routines in ODE models.

3.2 Performance of DFOs

Gradient-based methods outperform DFOs in the realistic ODE models in terms of converged runs. However, in cases where gradient-based methods failed, the particle swarm method PSWARM or the evolutionary method CMAES were reasonable alternatives, see Fig. 2 in the original publication.

4 Study design and evidence level

4.1 General aspects

You can describe general design aspects here. The study designs for describing specific outcomes are listed in the following subsections:

4.2 Design for Outcome O1

  • The outcome was generated for ...
  • Configuration parameters were chosen ...
  • ...

4.3 Design for Outcome O2

  • The outcome was generated for ...
  • Configuration parameters were chosen ...
  • ...

...

4.4 Design for Outcome O

  • The outcome was generated for ...
  • Configuration parameters were chosen ...
  • ...

5 Further comments and aspects

6 References

The list of cited or related literature is placed here.