Using Genetic Programming to Develop Inferential Estimation Algorithms
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Genetic Programming 1996: Proceedings of the First Annual Conference, page 157--165. Stanford University, CA, USA, MIT Press, (28--31 July 1996)

Genetic Programming (GP) is used to develop inferential estimation algorithms for two industrial chemical processes. Within this context, dynamic modelling procedures (as opposed to static or steady-state modelling) are often required if accurate inferential models are to be developed. Thus, a simple procedure is suggested so that the GP technique may be used for the development of dynamic process models. Using measurements from a vacuum distillation column and an industrial plasticating extrusion process, it is then demonstrated how the GP methodology can be used to develop reliable cost effective process models. A statistical analysis procedure is used to aid in the assessment of GP algorithm settings and to guide in the selection of the final model structure.
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