Crossover in Grammatical Evolution

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

@Article{oneill:2003:GPEM,
  author =       "Michael O'Neill and Conor Ryan and Maarten Keijzer and 
                 Mike Cattolico",
  title =        "Crossover in Grammatical Evolution",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2003",
  volume =       "4",
  number =       "1",
  pages =        "67--93",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, ripple crossover, homologous crossover,
                 headless chicken crossover, sub-tree crossover",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1023/A:1021877127167",
  abstract =     "We present an investigation into crossover in
                 Grammatical Evolution that begins by examining a
                 biologically-inspired homologous crossover operator
                 that is compared to standard one and two-point
                 operators. Results demonstrate that this homologous
                 operator is no better than the simpler one-point
                 operator traditionally adopted. An analysis of the
                 effectiveness of one-point crossover is then conducted
                 by determining the effects of this operator, by
                 adopting a headless chicken-type crossover that swaps
                 randomly generated fragments in place of the evolved
                 strings. Experiments show detrimental effects with the
                 utility of the headless chicken operator. Finally, the
                 mechanism of crossover in GE is analysed and termed
                 ripple crossover, due to its defining characteristics.
                 An experiment is described where ripple crossover is
                 applied to tree-based genetic programming, and the
                 results show that ripple crossover is more effective in
                 exploring the search space of possible programs than
                 sub-tree crossover 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 =        "Article ID: 5113073",
}

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

Citations