Is increased diversity in genetic programming beneficial? An analysis of the effects on performance

Created by W.Langdon from gp-bibliography.bib Revision:1.4394

  author =       "Edmund K. Burke and Steven Gustafson and 
                 Graham Kendall and Natalio Krasnogor",
  title =        "Is increased diversity in genetic programming
                 beneficial? An analysis of the effects on performance",
  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 =        "1398--1405",
  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, Convergence,
                 Entropy, Evolutionary computation, Shape, Stochastic
                 processes, artificial life, regression analysis,
                 artificial ant, binomial-3 function, even-5-parity,
                 genetic lineage selection, symbolic regression",
  ISBN =         "0-7803-7804-0",
  DOI =          "doi:10.1109/CEC.2003.1299834",
  abstract =     "A selection strategy based on genetic lineages is used
                 to increase genetic diversity. A genetic lineage is
                 defined as the path from an individual to individuals
                 which were created from its genetic material. The
                 method is applied to three problem domains: Artificial
                 Ant, Even-5-Parity and symbolic regression of the
                 Binomial-3 function. We examine how increased diversity
                 affects problems differently and draw conclusions about
                 the types of diversity which are more important for
                 each problem. Results indicate that diversity in the
                 Ant problem helps to overcome deception, while elitism
                 in combination with diversity is likely to benefit the
                 Parity and regression problems.",
  notes =        "CEC 2003 - A joint meeting of the IEEE, the IEAust,
                 the EPS, and the IEE.",

Genetic Programming entries for Edmund Burke Steven M Gustafson Graham Kendall Natalio Krasnogor