Complexity measures in Genetic Programming Learning: A Brief Review

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

@InProceedings{Le:2016:CEC,
  author =       "Nam Le and Hoai Nguyen Xuan and Anthony Brabazon and 
                 Thuong Pham Thi",
  title =        "Complexity measures in Genetic Programming Learning: A
                 Brief Review",
  booktitle =    "Proceedings of 2016 IEEE Congress on Evolutionary
                 Computation (CEC 2016)",
  year =         "2016",
  editor =       "Yew-Soon Ong",
  pages =        "2409--2416",
  address =      "Vancouver",
  month =        "24-29 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, Model
                 Selection, Vaknik-Chervonenkis dimension, Statistical
                 Machine Learning, Rademacher complexity",
  isbn13 =       "978-1-5090-0623-6",
  DOI =          "doi:10.1109/CEC.2016.7744087",
  abstract =     "Model complexity of Genetic Programming (GP) as a
                 learning machine is currently attracting considerable
                 interest from the research community. Here we provide
                 an up-to-date overview of the research concerning
                 complexity measure techniques in GP learning. The scope
                 of this review includes methods based on information
                 theory techniques, such as the Akaike Information
                 Criterion (AIC), Bayesian Information Criterion (BIC);
                 plus those based on statistical machine learning theory
                 on generalization error bound, namely,
                 Vapnik-Chervonenkis (VC) theory; and some based on
                 structural complexity. The research contributions from
                 each of these are systematically summarized and
                 compared, allowing us to clearly define existing
                 research challenges, and to highlight promising new
                 research directions. The findings of this review
                 provides valuable insights into the current GP
                 literature and is a good source for anyone who is
                 interested in the research on model complexity and
                 applying statistical learning theory to GP.",
  notes =        "WCCI2016",
}

Genetic Programming entries for Nam Le Nguyen Xuan Hoai Anthony Brabazon Thuong Pham Thi

Citations