Applying Boosting Techniques to Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Gregory Paris and Denis Robilliard and Cyril Fonlupt",
  title =        "Applying Boosting Techniques to Genetic Programming",
  booktitle =    "Artificial Evolution 5th International Conference,
                 Evolution Artificielle, EA 2001",
  year =         "2001",
  editor =       "P. Collet and C. Fonlupt and J.-K. Hao and 
                 E. Lutton and M. Schoenauer",
  volume =       "2310",
  series =       "LNCS",
  pages =        "267--278",
  address =      "Creusot, France",
  month =        oct # " 29-31",
  publisher =    "Springer Verlag",
  ISBN =         "3-540-43544-1",
  URL =          "",
  DOI =          "doi:10.1007/3-540-46033-0_22",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "This article deals with an improvement for genetic
                 programming based on a technique originating from the
                 machine learning field: boosting. In a first part of
                 this paper, we test the improvements offered by
                 boosting on binary problems. Then we propose to deal
                 with regression problems, and propose an algorithm,
                 called GPboost, that keeps closer to the original idea
                 of distribution in Adaboost than what has been done in
                 previous implementation of boosting for genetic
  notes =        "EA'01",

Genetic Programming entries for Gregory Paris Denis Robilliard Cyril Fonlupt