Genotype representations in grammatical evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Jonatan Hugosson and Erik Hemberg and 
                 Anthony Brabazon and Michael O'Neill",
  title =        "Genotype representations in grammatical evolution",
  journal =      "Applied Soft Computing",
  volume =       "10",
  number =       "1",
  pages =        "36--43",
  year =         "2010",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 evolution, Representation",
  DOI =          "doi:10.1016/j.asoc.2009.05.003",
  URL =          "",
  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. Two variants of genotype representation are
                 found in the literature, namely, binary and integer
                 forms. For the first time we analyse and compare these
                 two representations to determine if one has a
                 performance advantage over the other. As such 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,
                 are investigated. The differing combinations of
                 representation and mutation operator are tested on
                 three benchmark problems. The results provide support
                 for the use of an integer-based genotypic
                 representation as the alternative representations do
                 not exhibit better performance, and the integer
                 representation provides a statistically significant
                 advantage on one of the three benchmarks. In addition,
                 a novel wrapping operator for the binary and gray code
                 representations is examined, and it is found that
                 across the three problems examined there is no general
                 trend to recommend the adoption of an alternative
                 wrapping operator. The results also back up earlier
                 findings which support the adoption of wrapping.",

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