Evolving plural programs by genetic network programming with multi-start nodes

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Shingo Mabu and Kotaro Hirasawa",
  title =        "Evolving plural programs by genetic network
                 programming with multi-start nodes",
  booktitle =    "IEEE International Conference on Systems, Man and
                 Cybernetics, SMC 2009",
  year =         "2009",
  month =        oct,
  pages =        "1382--1387",
  keywords =     "genetic algorithms, genetic programming, automatic
                 program generation, directed graph structures,
                 even-n-parity problem, evolutionary computation,
                 genetic network programming, graph-based evolutionary
                 algorithm, mirror symmetry, multistart nodes,
                 performance evaluation, plural programs, automatic
                 programming, directed graphs",
  DOI =          "doi:10.1109/ICSMC.2009.5346275",
  ISSN =         "1062-922X",
  address =      "San Antonio, TX, USA",
  isbn13 =       "978-1-4244-2793-2",
  abstract =     "Automatic program generation is one of the applicable
                 fields of evolutionary computation, and genetic
                 programming (GP) is the typical method for this field.
                 On the other hand, genetic network programming (GNP)
                 has been proposed as an extended algorithm of GP in
                 terms of gene structures. GNP is a graph-based
                 evolutionary algorithm and applied to automatic program
                 generation in this paper. GNP has directed graph
                 structures which have some features inherently such as
                 re-usability of nodes and the fixed number of nodes.
                 These features contribute to creating complicated
                 programs with compact program structures. In this
                 paper, the extended algorithm of GNP is proposed, which
                 can create plural programs simultaneously in one
                 individual by using multi-start nodes. In addition, GNP
                 can evolve the programs in one individual considering
                 the fitness and also its standard deviation in order to
                 evolve the plural programs efficiently. In the
                 simulations, even-n-parity problem and mirror symmetry
                 problem are used for the performance evaluation, and
                 the results show that the proposed method outperforms
                 the original GNP.",
  notes =        "INSPEC Accession Number: 11004402 Also known as

Genetic Programming entries for Shingo Mabu Kotaro Hirasawa