- author = "Dominic Patrick Searson",
- title = "Non-linear {PLS} using genetic programming",
- school = "University of Newcastle upon Tyne",
- year = "2002",
- address = "UK",
- email = "d.p.searson@ncl.ac.uk",
- keywords = "genetic algorithms, genetic programming, multivariate models, multigene, co-evolution",
-
URL = "
http://www.staff.ncl.ac.uk/d.p.searson/docs/SearsonGP_PLS.pdf",
-
URL = "
http://ethos.bl.uk/OrderDetails.do?did=1&uin=uk.bl.ethos.246644",
-
abstract = "The economic and safe operation of modern industrial
process plants usually requires that accurate models of
the processes are available. Unfortunately, detailed
mathematical models of industrial process systems are
often time consuming and expensive to develop.
Consequently, the use of data based models is often the
only practical alternative. The need for effective
methods to build accurate data based models with a
minimum of specialist knowledge has given impetus to
the research of automatic model development methods.
One method, genetic programming (GP), which is an
evolutionary computational technique for automatically
learning how to solve problems, has previously been
identified as a candidate for automatic non-linear
model development. GP has also been combined with a
multivariate statistical regression method called PLS
(partial least squares) in order to improve its
performance (GP-PLS). One version of this method,
called GP_NPLS2, was found to give good performance but
at a computational expense deemed too high for use as a
modelling tool.
In this thesis, the GP-PLS framework is developed further. A novel architecture, called team based GP-PLS, is proposed. This method evolves teams of co-operating sub-models in parallel in an attempt to improve modelling performance without incurring significant additional computational expense. The performance of the team based method is compared with the original formulations of GP-PLS on steady state data sets from three synthetic test systems. Subsequently, a number of other modifications are made to the GP-PLS algorithms. These include the use of a multiple gene sub-model representation and a novel training method used to improve the ability of the evolved models to generalise to unseen data. Finally, an extended team method that encodes certain PLS parameters (the input projection weights) as binary team members is presented. The extended team method allows the optimisation of the sub-models and the projection weights simultaneously without recourse to computationally expensive iterative methods.",
- notes = "uk.bl.ethos.246644",
