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Abstract

In this contribution, genetic programming is combined with continuum regression to produce two novel nonlinear continuum regression algorithms. The first is a ‘sequential’ algorithm while the second adopts a ‘team-based’ strategy. Having discussed continuum regression, the modifications required to extend the algorithm for nonlinear modelling are outlined. The results of two case studies are then presented: the development of an inferential model of a food extrusion process and an input-output model of an industrial bioreactor. The superior performance of the sequential continuum regression algorithm, as compared to a similar sequential nonlinear partial least squares algorithm, is demonstrated. In addition, the studies clearly demonstrate that the team-based continuum regression strategy significantly outperforms both sequential approaches.

Keywords

co-evolution continuum regression genetic programming process modelling

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Nonlinear continuum regression: an evolutionary approach

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