A Rigorous Evaluation of Crossover and Mutation in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "David R. White and Simon Poulding",
  title =        "A Rigorous Evaluation of Crossover and Mutation in
                 Genetic Programming",
  booktitle =    "Proceedings of the 12th European Conference on Genetic
                 Programming, EuroGP 2009",
  year =         "2009",
  editor =       "Leonardo Vanneschi and Steven Gustafson and 
                 Alberto Moraglio and Ivanoe {De Falco} and Marc Ebner",
  volume =       "5481",
  series =       "LNCS",
  pages =        "220--231",
  address =      "Tuebingen",
  month =        apr # " 15-17",
  organisation = "EvoStar",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-01180-1",
  DOI =          "doi:10.1007/978-3-642-01181-8_19",
  URL =          "http://results.ref.ac.uk/Submissions/Output/1867870",
  size =         "12 pages",
  abstract =     "The role of crossover and mutation in Genetic
                 Programming (GP) has been the subject of much debate
                 since the emergence of the field. In this paper, we
                 contribute new empirical evidence to this argument
                 using a rigorous and principled experimental method
                 applied to six problems common in the GP literature.
                 The approach tunes the algorithm parameters to enable a
                 fair and objective comparison of two different GP
                 algorithms, the first using a combination of crossover
                 and reproduction, and secondly using a combination of
                 mutation and reproduction. We find that crossover does
                 not significantly outperform mutation on most of the
                 problems examined. In addition, we demonstrate that the
                 use of a straightforward Design of Experiments
                 methodology is effective at tuning GP algorithm
  notes =        "DOE, {"}millions of runs{"}, ECJ, factorial design.
                 Vargha-Delaney A statistic. DoE better than response
                 surface methodology and central composite design.

                 Part of \cite{conf/eurogp/2009} EuroGP'2009 held in
                 conjunction with EvoCOP2009, EvoBIO2009 and
  uk_research_excellence_2014 = "This was one of the first papers in GP
                 to employ highly rigorous empirical method, as well as
                 directly addressing a controversial topic (see Spector
                 and Luke's earlier works) using this approach.

                 It was the first to consider response surface
                 modelling, scientific significance, and apply the
                 Vargha-Delaney measure to GP performance.

                 The paper was short-listed for a best paper award at
                 EuroGP, the premier annual conference on Genetic

Genetic Programming entries for David Robert White Simon M Poulding