Modelling formulations using gene expression programming - A comparative analysis with artificial neural networks

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Colbourn2011366,
  author =       "E. A. Colbourn and S. J. Roskilly and R. C. Rowe and 
                 P. York",
  title =        "Modelling formulations using gene expression
                 programming - A comparative analysis with artificial
                 neural networks",
  journal =      "European Journal of Pharmaceutical Sciences",
  volume =       "44",
  number =       "3",
  pages =        "366--374",
  year =         "2011",
  ISSN =         "0928-0987",
  DOI =          "doi:10.1016/j.ejps.2011.08.021",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0928098711002958",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, Neural networks, Modelling,
                 Formulation",
  abstract =     "This study has investigated the utility and potential
                 advantages of gene expression programming (GEP) - a new
                 development in evolutionary computing for modelling
                 data and automatically generating equations that
                 describe the cause-and-effect relationships in a
                 system- to four types of pharmaceutical formulation and
                 compared the models with those generated by neural
                 networks, a technique now widely used in the
                 formulation development. Both methods were capable of
                 discovering subtle and non-linear relationships within
                 the data, with no requirement from the user to specify
                 the functional forms that should be used. Although the
                 neural networks rapidly developed models with higher
                 values for the ANOVA R2 these were black box and
                 provided little insight into the key relationships.
                 However, GEP, although significantly slower at
                 developing models, generated relatively simple
                 equations describing the relationships that could be
                 interpreted directly. The results indicate that GEP can
                 be considered an effective and efficient modelling
                 technique for formulation data.",
}

Genetic Programming entries for E A Colbourn S J Roskilly Raymond C Rowe Peter York

Citations