Ripple Crossover in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InProceedings{keijzer:2001:EuroGP,
  author =       "Maarten Keijzer and Conor Ryan and Michael O'Neill and 
                 Mike Cattolico and Vladan Babovic",
  title =        "Ripple Crossover in Genetic Programming",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and 
                 Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and 
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "74--86",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, Context Free Grammars, Crossover, Intrinsic
                 Polymorphism",
  ISBN =         "3-540-41899-7",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=74",
  size =         "13 pages",
  abstract =     "This paper isolates and identifies the effects of the
                 crossover operator used in Grammatical Evolution. This
                 crossover operator has already been shown to be adept
                 at combining useful building blocks and to outperform
                 engineered crossover operators such as Homologous
                 Crossover. This crossover operator, Ripple Crossover is
                 described in terms of Genetic Programming and applied
                 to two benchmark problems.

                 Its performance is compared with that of traditional
                 sub-tree crossover on populations employing the
                 standard functions and terminal set, but also against
                 populations of individuals that encode Context Free
                 Grammars. Ripple crossover is more effective in
                 exploring the search space of possible programs than
                 sub-tree crossover. This is shown by examining the rate
                 of premature convergence during the run. Ripple
                 crossover produces populations whose fitness increases
                 gradually over time, slower than, but to an eventual
                 higher level than that of sub-tree crossover.",
  notes =        "EuroGP'2001, part of \cite{miller:2001:gp}",
}

Genetic Programming entries for Maarten Keijzer Conor Ryan Michael O'Neill Mike Cattolico Vladan Babovic