Bloat free genetic programming: application to human oral bioavailability prediction

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

@Article{SaraSilva_11_2012,
  author =       "Sara Silva and Leonardo Vanneschi",
  title =        "Bloat free genetic programming: application to human
                 oral bioavailability prediction",
  journal =      "International Journal of Data Mining and
                 Bioinformatics",
  year =         "2012",
  volume =       "6",
  number =       "6",
  pages =        "585--601",
  month =        nov,
  publisher =    "Inderscience",
  keywords =     "genetic algorithms, genetic programming, bloat, code
                 growth, operator equalisation, data mining, drug
                 discovery, human oral bioavailability, prediction,
                 symbolic regression, overfitting, solution length,
                 feature selection",
  ISSN =         "1748-5681",
  bibdate =      "2012-11-27",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/ijdmb/ijdmb6.html#SilvaV12",
  URL =          "http://www.inderscience.com/dev/search/index.php?mainAction=search&action=record&rec_id=50266&prevQuery=&ps=10&m=or",
  broken =       "http://www.ingentaconnect.com/content/ind/ijdmb/2012/00000006/00000006/art00001",
  DOI =          "doi:10.1504/IJDMB.2012.050266",
  size =         "17 pages",
  abstract =     "Being able to predict the human oral bioavailability
                 for a potential new drug is extremely important for the
                 drug discovery process. This problem has been addressed
                 by several prediction tools, with Genetic Programming
                 providing some of the best results ever achieved. In
                 this paper we use the newest developments of Genetic
                 Programming, in particular the latest bloat control
                 method, Operator Equalisation, to find out how much
                 improvement we can achieve on this problem. We show
                 examples of some actual solutions and discuss their
                 quality, comparing them with previously published
                 results. We identify some unexpected behaviours related
                 to overfitting, and discuss the way for further
                 improving the practical usage of the Genetic
                 Programming approach.",
  notes =        "July 2014 Inderscience Publishers titles are no longer
                 available on ingentaconnect. PMID: 23356009",
}

Genetic Programming entries for Sara Silva Leonardo Vanneschi

Citations