Abstract
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 optimization problems. The genetic programming under discussion in this work relies on tree-like building blocks, and thus supports process modeling 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 behavior. 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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