Cartesian Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InProceedings{miller:2000:CGP,
  author =       "Julian F. Miller and Peter Thomson",
  title =        "Cartesian Genetic Programming",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and 
                 William B. Langdon and Julian F. Miller and Peter Nordin and 
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "121--132",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming",
  ISBN =         "3-540-67339-3",
  URL =          "http://www.elec.york.ac.uk/intsys/users/jfm7/cgp-eurogp2000.pdf",
  URL =          "http://citeseer.ist.psu.edu/424028.html",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=121",
  abstract =     "This paper presents a new form of Genetic Programming
                 called Cartesian Genetic Programming in which a program
                 is represented as an indexed graph. The graph is
                 encoded in the form of a linear string of integers. The
                 inputs or terminal set and node outputs are numbered
                 sequentially. The node functions are also separately
                 numbered. The genotype is just a list of node
                 connections and functions. The genotype is then mapped
                 to an indexed graph that can be executed as a program.
                 Evolutionary algorithms are used to evolve the genotype
                 in a symbolic regression problem (sixth order
                 polynomial) and the Santa Fe Ant Trail. The
                 computational effort is calculated for both cases. It
                 is suggested that hit effort is a more reliable measure
                 of computational efficiency. A neutral search strategy
                 that allows the fittest genotype to be replaced by
                 another equally fit genotype (a neutral genotype) is
                 examined and compared with non-neutral search for the
                 Santa Fe ant problem. The neutral search proves to be
                 much more effective.",
  notes =        "EuroGP'2000, part of \cite{poli:2000:GP}",
}

Genetic Programming entries for Julian F Miller Peter Thomson