GECCO 2012 tutorial: cartesian genetic programming

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

@InProceedings{Miller:2012:GECCOcomp,
  author =       "Julian Francis Miller and Simon Harding",
  title =        "GECCO 2012 tutorial: cartesian genetic programming",
  booktitle =    "GECCO 2012 Specialized techniques and applications
                 tutorials",
  year =         "2012",
  editor =       "Gabriela Ochoa",
  isbn13 =       "978-1-4503-1178-6",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming",
  pages =        "1093--1116",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Philadelphia, Pennsylvania, USA",
  DOI =          "doi:10.1145/2330784.2330932",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Cartesian Genetic Programming (CGP) is an increasingly
                 popular and efficient form of Genetic Programming that
                 was developed by Julian Miller in 1999 and 2000. In its
                 classic form, it uses a very simple integer based
                 genetic representation of a program in the form of a
                 directed graph. Graphs are very useful program
                 representations and can be applied to many domains
                 (e.g. electronic circuits, neural networks). In a
                 number of studies, CGP has been shown to be
                 comparatively efficient to other GP techniques. It is
                 also very simple to program.

                 Since then, the classical form of CGP has been
                 developed made more efficient in various ways. Notably,
                 by including automatically defined functions (modular
                 CGP) and self-modification operators (self-modifying
                 CGP). SMCGP was developed by Julian Miller, Simon
                 Harding and Wolfgang Banzhaf. It uses functions that
                 cause the evolved programs to change themselves as a
                 function of time. Using this technique it is possible
                 to find general solutions to classes of problems and
                 mathematical algorithms (e.g. arbitrary parity, n-bit
                 binary addition, sequences that provably compute pi and
                 e to arbitrary precision, and so on).

                 The tutorial will cover the basic technique, advanced
                 developments and applications to a variety of problem
                 domains.",
  notes =        "Also known as \cite{2330932} Distributed at
                 GECCO-2012.

                 ACM Order Number 910122.",
}

Genetic Programming entries for Julian F Miller Simon Harding

Citations