Name
Abstract | ||
|---|---|---|
The calibration of complex models of biological systems requires numer- ical simulation and optimization procedures to infer undetermined parameters and fit measured data. The optimization step typically employs heuristic global optimization algorithms, but due to measurement noise and the many degrees of freedom, it is not guaranteed that the identified single optimum is also the most meaningful parame- ter set. Multimodal optimization allows for identifying multiple optima in parallel. We consider high-dimensional benchmark functions and a realistic metabolic network model from systems biology to compare evolutionary and swarm-based multimodal methods. We show that an extended swarm based niching algorithm is able to find a considerable set of solutions in parallel, which have significantly more explanatory power. As an outline of the information gain, the variations in the set of high-quality solutions are contrasted to a state-of-the-art global sensitivity analysis. |
Year | Venue | Keywords |
|---|---|---|
2009 | GCB | information gain,global optimization,degree of freedom,network model,biological systems,metabolic network,system biology |
Field | DocType | Citations |
Computer simulation,Swarm behaviour,Computer science,Artificial intelligence,Heuristic,Mathematical optimization,Global optimization,Metabolic network,Systems biology,Genetics,Machine learning,Calibration,Network model | Conference | 4 |
PageRank | References | Authors |
0.73 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcel Kronfeld | 1 | 74 | 6.67 |
| Andreas Dräger | 2 | 292 | 22.16 |
| Moritz Aschoff | 3 | 14 | 1.83 |
| Andreas Zell | 4 | 1419 | 137.58 |