New crossover operators in linear genetic programming for multiclass object classification

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

@InProceedings{Downey:2010:gecco,
  author =       "Carlton Downey and Mengjie Zhang and Will N. Browne",
  title =        "New crossover operators in linear genetic programming
                 for multiclass object 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 =        "885--892",
  keywords =     "genetic algorithms, genetic programming",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Portland, Oregon, USA",
  DOI =          "doi:10.1145/1830483.1830644",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Genetic programming (GP) has been successfully applied
                 to solving multiclass classification problems, but the
                 performance of GP classifiers still lags behind that of
                 alternative techniques. This paper investigates an
                 alternative form of GP, Linear GP (LGP), which
                 demonstrates great promise as a classifier as the
                 division of classes is inherent in this technique. By
                 combining biological inspiration with detailed
                 knowledge of program structure two new crossover
                 operators that significantly improve performance are
                 developed. The first is a new crossover operator that
                 mimics biological crossover between alleles, which
                 helps reduce the disruptive effect on building blocks
                 of information. The second is an extension of the first
                 where a heuristic is used to predict offspring fitness
                 guiding search to promising solutions.",
  notes =        "Also known as \cite{1830644} 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 Carlton Downey Mengjie Zhang Will N Browne

Citations