Abstract
dentification of models for nonlinear dynamical
systems using multiobjective evolutionary algorithms.
Systems modelling involves the processes of structure
selection, parameter estimation, model performance and
model validation and involves a complex solution space.
Evolutionary Algorithms (EAs) are search and
optimisation tools founded on the principles of natural
evolution and genetics, which are suitable for a wide
range of application areas. Due to the versatility of
these tools and motivated by the versatility of genetic
programming (GP), this evolutionary paradigm is
proposed for this modelling problem. GP is then
combined with a multiobjective function definition
scheme. Multi objective genetic programming (MOGP) is
applied to multiple, conflicting objectives and yields
a set of candidate parsimonious and valid models, which
reproduce the original system behaviour. The MOGP
approach is then demonstrated as being applicable for
system modelling with chaotic dynamics. The circuit
introduced by Chua, being one of the most popular
benchmarks for studying nonlinear oscillations, and the
Duffing-Holmes oscillator are the systems to test the
evolutionary-based modelling
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