Coevolutionary multi-population genetic programming for data classification

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

@InProceedings{Augusto:2010:gecco,
  author =       "Douglas Adriano Augusto and 
                 Helio Jose Correa Barbosa and Nelson Francisco Favilla Ebecken",
  title =        "Coevolutionary multi-population genetic programming
                 for data classification",
  booktitle =    "GECCO '10: Proceedings of the 12th annual conference
                 on Genetic and evolutionary computation",
  year =         "2010",
  editor =       "Juergen Branke and Martin Pelikan and Enrique Alba and 
                 Dirk V. Arnold and Josh Bongard and 
                 Anthony Brabazon and Juergen Branke and Martin V. Butz and 
                 Jeff Clune and Myra Cohen and Kalyanmoy Deb and 
                 Andries P Engelbrecht and Natalio Krasnogor and 
                 Julian F. Miller and Michael O'Neill and Kumara Sastry and 
                 Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and 
                 Carsten Witt",
  isbn13 =       "978-1-4503-0072-8",
  pages =        "933--940",
  keywords =     "genetic algorithms, genetic programming, distributed
                 genetic programming",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Portland, Oregon, USA",
  DOI =          "doi:10.1145/1830483.1830650",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "This work presents a new evolutionary ensemble method
                 for data classification, which is inspired by the
                 concepts of bagging and boosting, and aims at combining
                 their good features while avoiding their weaknesses.
                 The approach is based on a distributed
                 multiple-population genetic programming (GP) algorithm
                 which exploits the technique of coevolution at two
                 levels. On the inter-population level the populations
                 cooperate in a semi-isolated fashion, whereas on the
                 intrapopulation level the candidate classifiers
                 coevolve competitively with the training data samples.
                 The final classifier is a voting committee composed by
                 the best members of all the populations. The
                 experiments performed in a varying number of
                 populations show that our approach outperforms both
                 bagging and boosting for a number of benchmark
                 problems.",
  notes =        "Also known as \cite{1830650} GECCO-2010 A joint
                 meeting of the nineteenth international conference on
                 genetic algorithms (ICGA-2010) and the fifteenth annual
                 genetic programming conference (GP-2010)",
}

Genetic Programming entries for Douglas A Augusto Helio J C Barbosa Nelson F F Ebecken

Citations