Evolving rule induction algorithms with multi-objective grammar-based genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Gisele L. Pappa and Alex A. Freitas",
  title =        "Evolving rule induction algorithms with
                 multi-objective grammar-based genetic programming",
  journal =      "Knowledge and Information Systems",
  year =         "2009",
  volume =       "19",
  number =       "3",
  pages =        "283--309",
  month =        jun,
  publisher =    "Springer",
  address =      "London",
  keywords =     "genetic algorithms, genetic programming, Grammar-based
                 genetic programming, Pareto optimisation, Rule
                 induction algorithms, Data mining, Classification",
  ISSN =         "0219-1377",
  DOI =          "doi:10.1007/s10115-008-0171-1",
  size =         "27 pages",
  abstract =     "Multi-objective optimisation has played a major role
                 in solving problems where two or more conflicting
                 objectives need to be simultaneously optimised. This
                 paper presents a Multi-Objective grammar-based genetic
                 programming (MOGGP) system that automatically evolves
                 complete rule induction algorithms, which in turn
                 produce both accurate and compact rule models. The
                 system was compared with a single objective GGP and
                 three other rule induction algorithms. In total, 20 UCI
                 data sets were used to generate and test generic rule
                 induction algorithms, which can be now applied to any
                 classification data set. Experiments showed that, in
                 general, the proposed MOGGP finds rule induction
                 algorithms with competitive predictive accuracies and
                 more compact models than the algorithms it was compared
  notes =        "Hyper-Heuristic",

Genetic Programming entries for Gisele L Pappa Alex Alves Freitas