An investigation of the mutation operator using different representations in Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Hugosson:2007:pliks,
  author =       "Jonatan Hugosson and Erik Hemberg and 
                 Anthony Brabazon and Michael O'Neill",
  title =        "An investigation of the mutation operator using
                 different representations in Grammatical Evolution",
  booktitle =    "2nd International Symposium {"}Advances in Artificial
                 Intelligence and Applications{"}",
  year =         "2007",
  volume =       "2",
  pages =        "409--419",
  address =      "Wisla, Poland",
  month =        oct # " 15-17",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  ISSN =         "1896 7094",
  URL =          "http://www.proceedings2007.imcsit.org/pliks/45.pdf",
  abstract =     "Grammatical evolution (GE) is a form of grammar-based
                 genetic programming. A particular feature of GE is that
                 it adopts a distinction between the genotype and
                 phenotype similar to that which exists in nature by
                 using a grammar to map between the genotype and
                 phenotype. This study seeks to extend our understanding
                 of GE by examining the impact of different genotypic
                 representations in order to determine whether certain
                 representations, and associated diversity-generation
                 operators, improve GE's efficiency and effectiveness.
                 Four mutation operators using two different
                 representations, binary and gray code representation
                 respectively, are investigated. The differing
                 combinations of representation and mutation operator
                 are tested on three benchmark problems. The results
                 provides support for the continued use of the standard
                 genotypic integer representation as the alternative
                 representations do not exhibit higher locality nor
                 better GE performance. The results raise the question
                 as to whether higher locality in GE actually improves
                 GE performance.",
}

Genetic Programming entries for Jonatan Hugosson Erik Hemberg Anthony Brabazon Michael O'Neill

Citations