A genetic programming-based QSPR model for predicting solubility parameters of polymers

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Dilek Imren Koc and Mehmet Levent Koc",
  title =        "A genetic programming-based {QSPR} model for
                 predicting solubility parameters of polymers",
  journal =      "Chemometrics and Intelligent Laboratory Systems",
  volume =       "144",
  pages =        "122--127",
  year =         "2015",
  ISSN =         "0169-7439",
  DOI =          "doi:10.1016/j.chemolab.2015.04.005",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0169743915000878",
  abstract =     "In this study, linear and nonlinear quantitative
                 structure-property relationship (QSPR) models,
                 respectively called the multiple linear regression
                 based QSPR (MLR-QSPR) model and the genetic programming
                 based QSPR (GP-QSPR) model, were built to predict the
                 solubility parameters of polymers with structure
                 -(C1H2-C2R3R4)-, as function of some constitutional,
                 topological and quantum chemical descriptors. The
                 results from the internal validation analysis indicated
                 that the GP-QSPR model has better goodness of fit
                 statistics. The external and overall validation
                 measures also confirmed that the GP-QSPR model
                 significantly outperforms the MLR-QSPR model in terms
                 of some performance metrics over the same testing data
                 set, and that genetic programming has good potential to
                 obtain more accurate models in QSPR studies.",
  keywords =     "genetic algorithms, genetic programming, Solubility
                 parameter, Polymers, Linear regression, QSPR",

Genetic Programming entries for Dilek Imren Koc Mehmet Levent Koc