Symbolic regression via genetic programming in the optimization of a controlled release pharmaceutical formulation

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Barmpalexis201175,
  author =       "P. Barmpalexis and K. Kachrimanis and A. Tsakonas and 
                 E. Georgarakis",
  title =        "Symbolic regression via genetic programming in the
                 optimization of a controlled release pharmaceutical
                 formulation",
  journal =      "Chemometrics and Intelligent Laboratory Systems",
  volume =       "107",
  number =       "1",
  pages =        "75--82",
  year =         "2011",
  ISSN =         "0169-7439",
  DOI =          "doi:10.1016/j.chemolab.2011.01.012",
  URL =          "http://www.sciencedirect.com/science/article/B6TFP-523CDG2-4/2/67c4e87b7f04a0e4f5f6fe07a1127ef8",
  keywords =     "genetic algorithms, genetic programming, Artificial
                 neural networks, Controlled release, Experimental
                 design, Optimisation",
  abstract =     "Symbolic regression via genetic programming (GP) was
                 used in the optimisation of a pharmaceutical zero-order
                 release matrix tablet, and its predictive performance
                 was compared to that of artificial neural network (ANN)
                 models. Two types of GP algorithms were employed: 1)
                 standard GP, where a single population is used with a
                 restricted or an extended function set, and 2)
                 multi-population (island model) GP, where a finite
                 number of populations is adopted. The amounts of four
                 polymers, namely PEG4000, PVP K30, HPMC K100 and HPMC
                 E50LV were selected as independent variables, while the
                 percentage of nimodipine released in 2 and 8 h (Y2h,
                 and Y8h), respectively, and the time at which 90% of
                 the drug was dissolved (t90%), were selected as
                 responses. Optimal models were selected by minimisation
                 of the Euclidian distance between predicted and optimum
                 release parameters. It was found that the prediction
                 ability of GP on an external validation set was higher
                 compared to that of the ANNs, with the multi population
                 and standard GP combined with an extended function set,
                 showing slightly better predictive performance.
                 Similarity factor (f2) values confirmed GP's increased
                 prediction performance for multi-population GP (f2 =
                 85.52) and standard GP using an extended function set
                 (f2 = 84.47).",
}

Genetic Programming entries for Panagiotis Barmpalexis Kyriakos Kachrimanis Athanasios D Tsakonas Emanouil Georgarakis

Citations