DGP: How To Improve Genetic Programming with Duals

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "J-L Segapeli and C. Escazut and P. Collard",
  title =        "DGP: How To Improve Genetic Programming with Duals",
  booktitle =    "Artificial Neural Nets and Genetic Algorithms:
                 Proceedings of the International Conference,
  year =         "1997",
  editor =       "George D. Smith and Nigel C. Steele and 
                 Rudolf F. Albrecht",
  pages =        "409--413",
  address =      "University of East Anglia, Norwich, UK",
  publisher =    "Springer-Verlag",
  note =         "published in 1998",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-211-83087-1",
  DOI =          "doi:10.1007/978-3-7091-6492-1_90",
  abstract =     "In this paper, we present a new approach, improving
                 the performances of a genetic algorithm (GA). Such
                 algorithms are iterative search procedures based on
                 natural genetics. We use an original genetic algorithm
                 that manipulates pairs of twins in its population: DGA,
                 dual-based genetic algorithm. We show that this
                 approach is relevant for genetic programming (GP),
                 which manipulates populations of trees. In particular,
                 we show that duals can transform a deceptive problem
                 into a convergent one. We also prove that using pairs
                 of dual functions in the primitive function set, is
                 more efficient in the problem of learning boolean
                 functions. Here, in order to prove the theoretical
                 interest of our approach (DGP: dual-based genetic
                 programming), we perform a numerical simulation.",
  notes =        "http://www.sys.uea.ac.uk/Research/ResGroups/MAG/ICANNGA97/papers_frame.html


Genetic Programming entries for J-L Segapeli Cathy Escazut Philippe Collard