Partial Functions in Fitness-Shared Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{mckay:2000:pffgp,
  author =       "Bob McKay",
  title =        "Partial Functions in Fitness-Shared Genetic
                 Programming",
  booktitle =    "Proceedings of the 2000 Congress on Evolutionary
                 Computation CEC00",
  year =         "2000",
  pages =        "349--356",
  volume =       "1",
  address =      "La Jolla Marriott Hotel La Jolla, California, USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, fitness,
                 accurate solutions, fitness sharing, multiplexer
                 definition learning, partial functions, performance,
                 population parameters, recursive list membership
                 function learning, total functions, functions, learning
                 (artificial intelligence), list processing,
                 multiplexing equipment, software performance
                 evaluation",
  ISBN =         "0-7803-6375-2",
  DOI =          "doi:10.1109/CEC.2000.870316",
  abstract =     "This paper investigates the use of partial functions
                 and fitness sharing in genetic programming. Fitness
                 sharing is applied to populations of either partial or
                 total functions and the results compared. Applications
                 to two classes of problem are investigated: learning
                 multiplexer definitions, and learning (recursive) list
                 membership functions. In both cases, fitness sharing
                 approaches outperform the use of raw fitness, by
                 generating more accurate solutions with the same
                 population parameters. On the list membership problem,
                 variants using fitness sharing on populations of
                 partial functions outperform variants using total
                 functions, whereas populations of total functions give
                 better performance on some variants of multiplexer
                 problems.",
  notes =        "CEC-2000 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 00TH8512,

                 Library of Congress Number = 00-018644",
}

Genetic Programming entries for R I (Bob) McKay

Citations