Program evolution with explicit learning: a New Framework for Program Automatic Synthesis

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InProceedings{Shan:2003:Pewel,
  author =       "Y. Shan and R. I. McKay and H. A. Abbass and 
                 D. Essam",
  title =        "Program evolution with explicit learning: a New
                 Framework for Program Automatic Synthesis",
  booktitle =    "Proceedings of the 2003 Congress on Evolutionary
                 Computation CEC2003",
  editor =       "Ruhul Sarker and Robert Reynolds and 
                 Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and 
                 Tom Gedeon",
  pages =        "1639--1646",
  year =         "2003",
  publisher =    "IEEE Press",
  address =      "Canberra",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "8-12 " # dec,
  organisation = "IEEE Neural Network Council (NNC), Engineers Australia
                 (IEAust), Evolutionary Programming Society (EPS),
                 Institution of Electrical Engineers (IEE)",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7803-7804-0",
  URL =          "http://www.cs.adfa.edu.au/~shanyin/publications/peel.pdf",
  URL =          "http://citeseer.ist.psu.edu/560804.html",
  abstract =     "In Genetic Programming (GP) and most of the other
                 evolutionary computing approaches, the knowledge which
                 is learned during the evolutionary processing is
                 implicitly encoded in the population. In this research,
                 we proposed a new approach for program synthesis --
                 Program Evolution with Explicit Learning (PEEL), which
                 learns and makes use of this knowledge explicitly. PEEL
                 learns probability distribution from previous
                 generations and stochastically generates new
                 populations according to this distribution. On the
                 benchmark problems we have studied, this approach can
                 synthesize more compact and more accurate programs than
                 GP.",
  notes =        "CEC 2003 - A joint meeting of the IEEE, the IEAust,
                 the EPS, and the IEE.",
}

Genetic Programming entries for Yin Shan R I (Bob) McKay Hussein A Abbass Daryl Essam