Nonlinear continuum regression: an evolutionary approach

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@Article{Mckay:2000:TIMC,

• author = "Ben McKay and Mark Willis and Dominic Searson and Gary Montague",
• title = "Nonlinear continuum regression: an evolutionary approach",
• journal = "Transactions of the Institute of Measurement and Control",
• year = "2000",
• volume = "22",
• number = "2",
• pages = "125--140",
• email = "mark.willis@ncl.ac.uk",
• keywords = "genetic algorithms, genetic programming, continuum regression, process modelling, co-evolution",
• DOI = "doi:10.1177/014233120002200202",
• abstract = "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 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. These applications clearly demonstrate that the team-based continuum regression strategy significantly outperforms both sequential approaches.",

}

Genetic Programming entries for Ben McKay Mark J Willis Dominic Patrick Searson Gary A Montague

Citations