Mutation vs. Crossover with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Wendy Ashlock",
  title =        "Mutation vs. Crossover with Genetic Programming",
  booktitle =    "ANNIE 2006, Intelligent Engineering Systems through
                 Artificial Neural Networks",
  year =         "2006",
  editor =       "Cihan H. Dagli and Anna L. Buczak and 
                 David L. Enke and Mark Embrechts and Okan Ersoy",
  volume =       "16",
  address =      "St. Louis, MO, USA",
  month =        nov # " 5-8",
  note =         "Part I: Evolutionary Computation",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "0791802566",
  DOI =          "doi:10.1115/1.802566.paper2",
  abstract =     "Understanding how variation operators work leads to a
                 better understanding both of the search space and of
                 the problem being solved. This study examines the
                 behaviour of mutation and crossover operators in
                 genetic programming using parse trees to find solutions
                 to 3-parity and 4-parity. The standard subtree
                 crossover and subtree mutation operators are studied
                 along with two new operators, fold mutation and fusion
                 crossover. They are studied in terms of how often and
                 how fast they solve the problem; how much they change
                 the fitness on average; and what proportion of
                 variations are neutral, harmful, and helpful. It is
                 found that operators behave differently when used alone
                 than when used together with another operator and that
                 some operators behave differently when solving 3-parity
                 and when solving 4-parity.",

Genetic Programming entries for Wendy Ashlock