Created by W.Langdon from gp-bibliography.bib Revision:1.8010
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.",
Genetic Programming entries for Dominic Patrick Searson