Non-linear continuum regression using genetic programming

Abstract

In this contribution, genetic programming is combined with continuum regression to produce two novel non-linear 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 non-linear modelling are outlined. The results of applying the derived non-linear algorithms to the development of an inferential model of a food extrusion process are then presented. The superior performance of the ‘sequential’ algorithm, as compared to a similar non-linear partial least squares algorithm, is demonstrated. In addition, these results clearly demonstrate that the ‘team-based ’ strategy significantly outperforms the ‘sequential ’ approach. 1