Controlling Overfitting in Symbolic Regression Based on a Bias/Variance Error Decomposition

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Alexandros Agapitos and Anthony Brabazon and 
                 Michael O'Neill",
  title =        "Controlling Overfitting in Symbolic Regression Based
                 on a Bias/Variance Error Decomposition",
  booktitle =    "Parallel Problem Solving from Nature, PPSN XII (part
  year =         "2012",
  editor =       "Carlos A. {Coello Coello} and Vincenzo Cutello and 
                 Kalyanmoy Deb and Stephanie Forrest and 
                 Giuseppe Nicosia and Mario Pavone",
  volume =       "7491",
  series =       "Lecture Notes in Computer Science",
  pages =        "438--447",
  address =      "Taormina, Italy",
  month =        sep # " 1-5",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-32936-4",
  DOI =          "doi:10.1007/978-3-642-32937-1_44",
  size =         "10 pages",
  abstract =     "We consider the fundamental property of generalisation
                 of data-driven models evolved by means of Genetic
                 Programming (GP). The statistical treatment of
                 decomposing the regression error into bias and variance
                 terms provides insight into the generalisation
                 capability of this modelling method. The error
                 decomposition is used as a source of inspiration to
                 design a fitness function that relaxes the sensitivity
                 of an evolved model to a particular training dataset.
                 Results on eight symbolic regression problems show that
                 new method is capable on inducing better-generalising
                 models than standard GP for most of the problems.",
  affiliation =  "Natural Computing Research and Applications Group,
                 University College Dublin, Ireland",

Genetic Programming entries for Alexandros Agapitos Anthony Brabazon Michael O'Neill