Validation Sets for Evolutionary Curtailment with Improved Generalisation

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Jeannie Fitzgerald and Conor Ryan",
  title =        "Validation Sets for Evolutionary Curtailment with
                 Improved Generalisation",
  booktitle =    "5th International Conference on Convergence and Hybrid
                 Information Technology, ICHIT 2011",
  year =         "2011",
  editor =       "Geuk Lee and Daniel Howard and Dominik Slezak",
  volume =       "6935",
  series =       "Lecture Notes in Computer Science",
  pages =        "282--289",
  address =      "Daejeon, Korea",
  month =        sep # " 22-24",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-24081-2",
  DOI =          "doi:10.1007/978-3-642-24082-9_35",
  size =         "8 page",
  abstract =     "This paper investigates the leveraging of a validation
                 data set with Genetic Programming (GP) to counteract
                 over-fitting. It considers fitness on both training and
                 validation fitness, combined with with an early
                 stopping mechanism to improve generalisation while
                 significantly reducing run times. The method is tested
                 on six benchmark binary classification data sets.
                 Results of this preliminary investigation suggest that
                 the strategy can deliver equivalent or improved results
                 on test data.",
  notes =        "ICHIT (1)",
  affiliation =  "Jeannie Fitzgerald, BDS Group, CSIS Department,
                 University of Limerick, Ireland",

Genetic Programming entries for Jeannie Fitzgerald Conor Ryan