Bagging, Boosting, and Bloating in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Hitoshi Iba",
  title =        "Bagging, Boosting, and Bloating in Genetic
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and 
                 Agoston E. Eiben and Max H. Garzon and Vasant Honavar and 
                 Mark Jakiela and Robert E. Smith",
  volume =       "2",
  pages =        "1053--1060",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, classifier
  ISBN =         "1-55860-611-4",
  URL =          "",
  URL =          "",
  abstract =     "subpopulations",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)

                 10 Subpopulations each has its own training data
                 (produced using the boosting or bagging methods. Best
                 of each subpopulation has vote in final result. Do we
                 actually need subpopulations, could not the whole
                 algorithm be split into T entirely separate GP runs?

                 p1054 {"}controlling the bloating effect is closely
                 related to the performance improvement...{"}

                 noisy cos(2x)=1-sin(x)**2, Mackey-Glass chaotic time
                 series, 6MUX, symbolic regression, nikkei225
                 Description of boosting weight adjustment algorithm
                 (p1054) seems to be wrong?

                 p1056 BagGP, BoostGP > GP, BagGP=BoostGP But only in
                 the case of noisy cos(2x) does difference (table 2)
                 seem big. Mention of DSS and PADO.

                 p1059 Says Bagging and Boosting yield lower bloat.
                 (does not explain why) Little supporting data (Fig 5).
                 Boosting v co-evolution",

Genetic Programming entries for Hitoshi Iba