Self modifying cartesian genetic programming: finding algorithms that calculate pi and e to arbitrary precision

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

@InProceedings{Harding:2010:gecco,
  author =       "Simon Harding and Julian F. Miller and 
                 Wolfgang Banzhaf",
  title =        "Self modifying cartesian genetic programming: finding
                 algorithms that calculate pi and e to arbitrary
                 precision",
  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 =        "579--586",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming, Generative and developmental
                 systems",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Portland, Oregon, USA",
  DOI =          "doi:10.1145/1830483.1830591",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Self Modifying Cartesian Genetic Programming (SMCGP)
                 aims to be a general purpose form of developmental
                 genetic programming. The evolved programs are iterated
                 thus allowing an infinite sequence of phenotypes
                 (programs) to be obtained from a single evolved
                 genotype. In previous work this approach has already
                 shown that it is possible to obtain mathematically
                 provable general solutions to certain problems. We
                 extend this class in this paper by showing how SMCGP
                 can be used to find algorithms that converge to
                 mathematical constants (pi and e). Mathematical proofs
                 are given that show that some evolved formulae converge
                 to pi and e in the limit as the number of iterations
                 increase.",
  notes =        "Also known as \cite{1830591} 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 Simon Harding Julian F Miller Wolfgang Banzhaf

Citations