- author = "Knut Bernhardt",
- title = "Finding Alternatives and Reduced Formulations for Process-Based Models",
- journal = "Evolutionary Computation",
- year = "2008",
- volume = "16",
- number = "1",
- pages = "63--88",
- month = "Spring",
- keywords = "genetic algorithms, genetic programming, Model reduction, complexity, dimension reduction",
- ISSN = "1063-6560",
-
DOI = "
doi:10.1162/evco.2008.16.1.63",
- size = "26 pages",
-
abstract = "This paper addresses the problem of model complexity
commonly arising in constructing and using
process-based models with intricate interactions. Apart
from complex process details the dynamic behaviour of
such systems is often limited to a discrete number of
typical states. Thus, models reproducing the system's
processes in all details are often too complex and
over-parameterised. In order to reduce simulation times
and to get a better impression of the important
mechanisms, simplified formulations are desirable.
In this work a data adaptive model reduction scheme that automatically builds simple models from complex ones is proposed. The method can be applied to the transformation and reduction of systems of ordinary differential equations. It consists of a multistep approach using a low dimensional projection of the model data followed by a Genetic Programming/Genetic Algorithm hybrid to evolve new model systems. As the resulting models again consist of differential equations, their process-based interpretation in terms of new state variables becomes possible.
Transformations of two simple models with oscillatory dynamics, simulating a mathematical pendulum and predator-prey interactions respectively, serve as introductory examples of the method's application. The resulting equations of force indicate the predator-prey system's equivalence to a nonlinear oscillator. In contrast to the simple pendulum it contains driving and damping forces that produce a stable limit cycle.",
- notes = "CVODE, SUNDIALS",
