Using a Distance Metric on Genetic Programs to Understand Genetic Operators

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

@Unpublished{oreilly:1997:dmGPugo,
  author =       "Una-May O'Reilly",
  title =        "Using a Distance Metric on Genetic Programs to
                 Understand Genetic Operators",
  editor =       "Wolfgang Banzhaf and Inman Harvey and Hitoshi Iba and 
                 William Langdon and Una-May O'Reilly and 
                 Justinian Rosca and Byoung-Tak Zhang",
  note =         "Position paper at the Workshop on Evolutionary
                 Computation with Variable Size Representation at
                 ICGA-97",
  month =        "20 " # jul,
  year =         "1997",
  address =      "East Lansing, MI, USA",
  keywords =     "genetic algorithms, genetic programming, variable size
                 representation",
  abstract =     "I describe a distance metric called ''edit'' distance
                 which quantifies the syntactic difference between two
                 genetic programs. In the context of one specific
                 problem, the 6 bit multiplexor, I use the metric to
                 analyze the amount of new material introduced by
                 different crossover operators, the difference among the
                 best individuals of a population and the difference
                 among the best individuals and the rest of the
                 population. The relationships between these data and
                 run performance are imprecise but they are sufficiently
                 interesting to encourage encourage further
                 investigation into the use of edit distance.",
  notes =        "http://web.archive.org/web/19971014081458/http://www.ai.mit.edu/people/unamay/icga-ws.html

                 ",
  size =         "4 pages. See \cite{oreilly:1997:dnGPugo2}",
}

Genetic Programming entries for Una-May O'Reilly

Citations