Abstract functions and lifetime learning in genetic programming for symbolic regression

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "R. Muhammad Atif Azad and Conor Ryan",
  title =        "Abstract functions and lifetime learning in genetic
                 programming for symbolic regression",
  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 =        "893--900",
  keywords =     "genetic algorithms, genetic programming",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Portland, Oregon, USA",
  DOI =          "doi:10.1145/1830483.1830645",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Typically, an individual in Genetic Programming (GP)
                 can not make the most of its genetic inheritance. Once
                 it is mapped, its fitness is immediately evaluated and
                 it survives only until the genetic operators and its
                 competitors eliminate it. Thus, the key to survival is
                 to be born strong.

                 This paper proposes a simple alternative to this
                 powerlessness by allowing an individual to tune its
                 internal nodes and going through several evaluations
                 before it has to compete with other individuals.

                 We demonstrate that this system, Chameleon, outperforms
                 standard GP over a selection of symbolic regression
                 type problems on both training and test sets; that the
                 system works harmoniously with two other well known
                 extensions to GP, that is, linear scaling and a
                 diversity promoting tournament selection method; that
                 it can benefit dramatically from a simple cache; that
                 adding to functions set does not always add to the
                 tuning expense; and that tuning alone can be enough to
                 promote smaller trees in the population. Finally, we
                 touch upon the consequences of ignoring the effects of
                 complexity when focusing on just the tree sizes to
                 induce parsimony pressure in GP populations.",
  notes =        "Also known as \cite{1830645} 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 R Muhammad Atif Azad Conor Ryan