@INPROCEEDINGS{Kronfeld2009,
  author = {Kronfeld, Marcel and Aschoff, Moritz and Dr{\"a}ger, Andreas and
	Zell, Andreas},
  title = {{On the Benefits of Multimodal Optimization of a Metabolic Network Model}},
  booktitle = {German Conference on Bioinformatics (GCB 2009)},
  year = {2009},
  editor = {Grosse, Ivo and Neumann, Steffen and Posch, Stefan and Schreiber,
	Falk and Stadler, Peter},
  volume = {P-157},
  number = {978-3-88579-251-2},
  series = {Lecture Notes in Informatics},
  publisher = {German Informatics society},
  pages = {191-200},
  address = {Halle (Saale), Germany},
  month = {September},
  abstract = {The calibration of complex models of biological systems
    requires numerical 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
    parameter 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.}
}