A Genetic Programming Approach to Structural Identification of Cellular Automata

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Samira {El Yacoubi} and Przemyslaw Jacewicz",
  title =        "A Genetic Programming Approach to Structural
                 Identification of Cellular Automata",
  journal =      "Journal of Cellular Automata",
  year =         "2007",
  volume =       "2",
  number =       "1",
  pages =        "67--76",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1557-5969",
  URL =          "http://www.oldcitypublishing.com/journals/jca-home/jca-issue-contents/jca-volume-2-number-1-2007/jca-2-1-p-67-76/",
  broken =       "http://www.oldcitypublishing.com/JCA/JCAabstracts/JCA2.1abstracts/JCAv2n1p67-76Yacoubi.html",
  abstract =     "As is well-known, it is very hard to design local
                 state transition rules in cellular automata (CAs) in
                 order to perform a pre-specified global task, as it is
                 difficult to pass from the usual microscopic
                 specification of the automaton to an appropriate
                 description of its global behaviour. Our paper aims at
                 demonstrating a possibility of finding the best state
                 transition rules, along with the corresponding
                 neighbourhood, in order for a CA to accomplish a given
                 assignment, by means of genetic programming. Genetic
                 programming is an extension of classical genetic
                 algorithms in which computer programs are genetically
                 bred to solve problems. The introduced ideas are
                 illustrated by some simulation examples regarding
                 solving one-dimensional density and synchronisation
  bibsource =    "DBLP, http://dblp.uni-trier.de",

Genetic Programming entries for Samira El Yacoubi Przemyslaw Jacewicz