Forecasting with genetically programmed polynomial neural networks

Created by W.Langdon from gp-bibliography.bib Revision:1.4333

  author =       "Lilian M. {de Menezes} and Nikolay Y. Nikolaev",
  title =        "Forecasting with genetically programmed polynomial
                 neural networks",
  journal =      "International Journal of Forecasting",
  year =         "2006",
  volume =       "22",
  number =       "2",
  pages =        "249--265",
  month =        apr # "-" # jun,
  keywords =     "genetic algorithms, genetic programming, Nonlinear
                 models, Tree-structured polynomial neural network
                 models, Statistical learning algorithms",
  DOI =          "doi:10.1016/j.ijforecast.2005.05.002",
  abstract =     "Recent literature on nonlinear models has shown
                 genetic programming to be a potential tool for
                 forecasters. A special type of genetically programmed
                 model, namely polynomial neural networks, is addressed.
                 Their outputs are polynomials and, as such, they are
                 open boxes that are amenable to comprehension,
                 analysis, and interpretation.

                 This paper presents a polynomial neural network
                 forecasting system, PGP, which has three innovative
                 features: polynomial block reformulation, local ridge
                 regression for weight estimation, and regularised
                 weight subset selection for pruning that uses a least
                 absolute shrinkage and selection operator. The relative
                 performance of this system to other established
                 forecasting procedures is the focus of this research
                 and is illustrated by three empirical studies. Overall,
                 the results are very promising and indicate areas for
                 further research.",

Genetic Programming entries for Lilian M de Menezes Nikolay Nikolaev