A probabilistic functional crossover operator for genetic programming

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

  author =       "Josh C. Bongard",
  title =        "A probabilistic functional crossover operator for
                 genetic programming",
  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 =        "925--932",
  keywords =     "genetic algorithms, genetic programming",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Portland, Oregon, USA",
  DOI =          "doi:10.1145/1830483.1830649",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "The original mechanism by which evolutionary
                 algorithms were to solve problems was to allow for the
                 gradual discovery of sub-solutions to sub-problems, and
                 the automated combination of these sub-solutions into
                 larger solutions. This latter property is particularly
                 challenging when recombination is performed on genomes
                 encoded as trees, as crossover events tend to greatly
                 alter the original genomes and therefore greatly reduce
                 the chance of the crossover event being beneficial. A
                 number of crossover operators designed for tree-based
                 genetic encodings have been proposed, but most consider
                 crossing genetic components based on their structural
                 similarity. In this work we introduce a tree-based
                 crossover operator that probabilistically crosses
                 branches based on the behavioural similarity between
                 the branches. It is shown that this method outperforms
                 genetic programming without crossover, random
                 crossover, and a deterministic form of the crossover
                 operator in the symbolic regression domain.",
  notes =        "Also known as \cite{1830649} 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 Josh C Bongard