A comparison of the generalization ability of different genetic programming frameworks

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "Mauro Castelli and Luca Manzoni and Sara Silva and 
                 Leonardo Vanneschi",
  title =        "A comparison of the generalization ability of
                 different genetic programming frameworks",
  booktitle =    "IEEE Congress on Evolutionary Computation (CEC 2010)",
  year =         "2010",
  address =      "Barcelona, Spain",
  month =        "18-23 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4244-6910-9",
  abstract =     "Generalisation is an important issue in machine
                 learning. In fact, in several applications good results
                 over training data are not as important as good results
                 over unseen data. While this problem was deeply studied
                 in other machine learning techniques, it has become an
                 important issue for genetic programming only in the
                 last few years. In this paper we compare the
                 generalization ability of several different genetic
                 programming frameworks, including some variants of
                 multi-objective genetic programming and operator
                 equalisation, a recently defined bloat free genetic
                 programming system. The test problem used is a hard
                 regression real-life application in the field of drug
                 discovery and development, characterised by a high
                 number of features and where the generalisation ability
                 of the proposed solutions is a crucial issue. The
                 results we obtained show that, at least for the
                 considered problem, multi-optimization is effective in
                 improving genetic programming generalization ability,
                 outperforming all the other methods on test data.",
  DOI =          "doi:10.1109/CEC.2010.5585925",
  notes =        "WCCI 2010. Also known as \cite{5585925}",

Genetic Programming entries for Mauro Castelli Luca Manzoni Sara Silva Leonardo Vanneschi