Parameter evaluation of geometric semantic genetic programming in pharmacokinetics

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

@Article{Castelli:2016:IJBIC,
  title =        "Parameter evaluation of geometric semantic genetic
                 programming in pharmacokinetics",
  author =       "Mauro Castelli and Leonardo Vanneschi and 
                 Ales Popovic",
  journal =      "Int. J. of Bio-Inspired Computation",
  year =         "2016",
  month =        feb # "~10",
  volume =       "8",
  number =       "1",
  pages =        "42--50",
  keywords =     "genetic algorithms, genetic programming, semantics,
                 geometric semantic operators, regression, parameter
                 evaluation, pharmacokinetics, semantic crossover,
                 semantic mutation, drug discovery",
  publisher =    "Inderscience Publishers",
  ISSN =         "1758-0374",
  bibsource =    "OAI-PMH server at www.inderscience.com",
  language =     "eng",
  URL =          "http://www.inderscience.com/link.php?id=74634",
  DOI =          "DOI:10.1504/IJBIC.2016.074634",
  abstract =     "The role of crossover and mutation in genetic
                 programming has been the subject of much debate since
                 the emergence of the field. Recently new genetic
                 operators, called geometric semantic operators, have
                 been introduced. Contrary to standard genetic
                 operators, these operators present the interesting
                 property of inducing a unimodal fitness landscape for
                 every problem that consists in finding a match between
                 inputs and targets. As the definition of these
                 operators is quite recent, their effect on the
                 evolutionary dynamics is still in many senses unknown
                 and deserves to be studied. This paper intends to fill
                 this gap, with a specific focus on applications in the
                 field of pharmacokinetic. Results show that a mixture
                 of semantic crossover and mutation is always beneficial
                 compared to the use of only one of these operators.
                 Furthermore, we show that the best results are obtained
                 using values of the semantic mutation rate which are
                 considerably higher than the ones that are typically
                 used when traditional subtree mutation is employed.",
}

Genetic Programming entries for Mauro Castelli Leonardo Vanneschi Ales Popovic

Citations