An Investigation of Fitness Sharing in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{McKay:2001:AJIIPS_1,
  author =       "R. I. (Bob) McKay",
  journal =      "The Australian Journal of Intelligent Information
                 Processing Systems",
  month =        jul,
  number =       "1/2",
  pages =        "43--51",
  title =        "An Investigation of Fitness Sharing in Genetic
                 Programming",
  URL =          "http://sc.snu.ac.kr/PAPERS/AJIIPSfitshr.pdf",
  volume =       "7",
  year =         "2001",
  keywords =     "genetic algorithms, genetic programming",
  size =         "8 pages",
  abstract =     "This paper investigates fitness sharing in genetic
                 programming. Implicit fitness sharing is applied to
                 populations of programs. Three treatments are compared:
                 raw fitness, pure fitness sharing, and a gradual change
                 from fitness sharing to raw fitness. The 6- and
                 11-multiplexer problems are compared. Using the same
                 population sizes, fitness sharing shows a large
                 improvement in the error rate for both problems.
                 Further experiments compare the treatments on learning
                 recursive list membership functions; again, there are
                 dramatic improvements in error rate. Conversely,
                 fitness sharing runs achieve comparable results to raw
                 fitness using populations two to three times smaller.
                 Measures of population diversity suggest that the
                 results are due to preservation of diversity and
                 avoidance of premature convergence by the fitness
                 sharing runs.",
}

Genetic Programming entries for R I (Bob) McKay

Citations