Investigating Mapping Order in piGE

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

@InProceedings{fagan_etal:cec2010,
  author =       "David Fagan and Miguel Nicolau and Michael O'Neill and 
                 Edgar Galvan-Lopez and Anthony Brabazon and 
                 Sean McGarraghy",
  title =        "Investigating Mapping Order in piGE",
  booktitle =    "2010 IEEE World Congress on Computational
                 Intelligence",
  pages =        "3058--3064",
  year =         "2010",
  address =      "Barcelona, Spain",
  month =        "18-23 " # jul,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  isbn13 =       "978-1-4244-6910-9",
  DOI =          "doi:10.1109/CEC.2010.5586204",
  abstract =     "We present an investigation into the
                 genotype-phenotype map in Position Independent
                 Grammatical Evolution (piGE). Previous studies have
                 shown piGE to exhibit a performance increase over
                 standard GE. The only difference between the two
                 approaches is in how the genotype-phenotype mapping
                 process is performed. GE uses a leftmost non terminal
                 expansion, while piGE evolves the order of mapping as
                 well as the content. In this study, we use the idea of
                 focused search to examine which aspect of the piGE
                 mapping process provides the lift in performance over
                 standard GE by applying our approaches to four
                 benchmark problems taken from specialised literature.
                 We examined the traditional piGE approach and compared
                 it to two setups which examined the extremes of mapping
                 order search and content search, and against setups
                 with varying ratios of content and order search. In all
                 of these tests a purely content focused piGE was shown
                 to exhibit a performance gain over the other setups.",
  notes =        "WCCI 2010. Also known as \cite{5586204}",
}

Genetic Programming entries for David Fagan Miguel Nicolau Michael O'Neill Edgar Galvan Lopez Anthony Brabazon Sean McGarraghy

Citations