An empirical study of the efficiency of learning boolean functions using a Cartesian Genetic Programming approach

Created by W.Langdon from gp-bibliography.bib Revision:1.4128

@InProceedings{miller:1999:ACGP,
  author =       "Julian F. Miller",
  title =        "An empirical study of the efficiency of learning
                 {boolean} functions using a Cartesian Genetic
                 Programming approach",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and 
                 Agoston E. Eiben and Max H. Garzon and Vasant Honavar and 
                 Mark Jakiela and Robert E. Smith",
  volume =       "2",
  pages =        "1135--1142",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming, evolvable hardware",
  ISBN =         "1-55860-611-4",
  URL =          "http://citeseer.ist.psu.edu/153431.html",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-411.ps",
  abstract =     "A new form of Genetic Programming (GP) called
                 Cartesian Genetic Programming (CGP) is proposed in
                 which programs are represented by linear integer
                 chromosomes in the form of connections and
                 functionalities of a rectangular array of primitive
                 functions. The effectiveness of this approach is
                 investigated for boolean even-parity functions (3,4,5),
                 and the 2-bit multiplier. The minimum number of
                 evaluations required to give a 0.99 probability of
                 evolving a target function is used to measure the
                 efficiency of the new approach. It is found that
                 extremely low populations are most effective. A simple
                 probabilistic hillclimber (PH) is devised which proves
                 to be even more effective. For these boolean functions
                 either method appears to be much more efficient than
                 the GP and Evolutionary Programming (EP) methods
                 reported. The efficacy of the PH suggests that boolean
                 function learning may not be an appropriate problem for
                 testing the effectiveness of GP and EP.",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

Genetic Programming entries for Julian F Miller

Citations