Zusammenfassung
Genetic programming is one of the computer algorithms
in the family of evolutionary-computational methods,
which have been shown to provide reliable solutions to
complex optimisation problems. The genetic programming
under discussion in this work relies on tree-like
building blocks, and thus supports process modelling
with varying structure. This paper, which describes an
improved GP to facilitate the generation of
steady-state nonlinear empirical models for process
analysis and optimization, is an evolution of several
works in the field. The key feature of the method is
its ability to adjust the complexity of the required
model to accurately predict the true process behaviour.
The improved GP code incorporates a novel fitness
calculation, the optimal creation of new generations,
and parameter allocation. The advantages of these
modifications are tested against the more commonly used
approaches.
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