Multiobjective Parsimony Enforcement for Superior Generalisation Performance

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  title =        "Multiobjective Parsimony Enforcement for Superior
                 Generalisation Performance",
  author =       "Yaniv Bernstein and Xiaodong Li and Vic Ciesielski and 
                 Andy Song",
  pages =        "83--89",
  booktitle =    "Proceedings of the 2004 IEEE Congress on Evolutionary
  year =         "2004",
  publisher =    "IEEE Press",
  month =        "20-23 " # jun,
  address =      "Portland, Oregon",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming,
                 Multiobjective evolutionary algorithms, Combinatorial
                 \& numerical optimization",
  URL =          "",
  DOI =          "doi:10.1109/CEC.2004.1330841",
  size =         "7 pages",
  abstract =     "Program Bloat - the phenomenon of ever-increasing
                 program size during a GP run - is a recognised and
                 widespread problem. Traditional techniques to combat
                 program bloat are program size limitations or parsimony
                 pressure (penalty functions). These techniques suffer
                 from a number of problems, in particular their reliance
                 on parameters whose optimal values it is difficult to a
                 priori determine. In this paper we introduce POPE-GP, a
                 system that makes use of the NSGA-II multiobjective
                 evolutionary algorithm as an alternative,
                 parameter-free technique for eliminating program bloat.
                 We test it on a classification problem and find that
                 while vastly reducing program size, it does improve
                 generalisation performance.",
  notes =        "CEC 2004 - A joint meeting of the IEEE, the EPS, and
                 the IEE.",

Genetic Programming entries for Yaniv Bernstein Xiaodong Li Victor Ciesielski Andy Song