Evolving a Learning Machine by Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Eva Alfaro-Cid and Ken Sharman and 
                 Anna I. Esparcia-Alcazar",
  title =        "Evolving a Learning Machine by Genetic Programming",
  booktitle =    "Proceedings of the 2006 IEEE Congress on Evolutionary
  year =         "2006",
  editor =       "Gary G. Yen and Lipo Wang and Piero Bonissone and 
                 Simon M. Lucas",
  pages =        "958--962",
  address =      "Vancouver",
  month =        "6-21 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, simulated
                 annealing, function set, learning machine, learning
                 node, optimization algorithm, simulated annealing",
  ISBN =         "0-7803-9487-9",
  DOI =          "doi:10.1109/CEC.2006.1688316",
  size =         "5 pages",
  abstract =     "We describe a novel technique for evolving a machine
                 that can learn. The machine is evolved using a Genetic
                 Programming (GP) algorithm that incorporates in its
                 function set what we have called a learning node. Such
                 a node is tuned by a second optimisation algorithm (in
                 this case Simulated Annealing), mimicking a natural
                 learning process and providing the GP tree with added
                 flexibility and adaptability. The result of the
                 evolution is a system with a fixed structure but with
                 some variable parameters. The system can then learn new
                 tasks in new environments without undergoing further
  notes =        "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
                 the IEE.

                 IEEE Catalog Number: 06TH8846D IEEE Xplore gives pages
                 as 254--258",

Genetic Programming entries for Eva Alfaro-Cid Kenneth C Sharman Anna Esparcia-Alcazar