Evolving encapsulated programs as shared grammars

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

@Article{Luerssen:2008:GPEM,
  author =       "Martin H. Luerssen and David M. W. Powers",
  title =        "Evolving encapsulated programs as shared grammars",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2008",
  volume =       "9",
  number =       "3",
  pages =        "203--228",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, Shared
                 grammars, Developmental systems, Encapsulation,
                 Modularity, Memoization",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-008-9061-2",
  abstract =     "Facilitating the discovery and reuse of modular
                 building blocks is generally regarded as the key to
                 achieving better scalability in genetic programming
                 (GP). A precedent for this exists in biology, where
                 complex designs are the product of developmental
                 processes that can also be abstractly modelled as
                 generative grammars. We introduce shared grammar
                 evolution (SGE), which aligns grammatical development
                 with the common application of grammars in GP as a
                 means of establishing declarative bias. Programs are
                 derived from and represented by a global context-free
                 grammar that is transformed and extended according to
                 another, user-defined grammar. Grammatical productions
                 and the subroutines they encapsulate are shared between
                 programs, which enables their reuse without
                 reevaluation and can significantly reduce total
                 evaluation time for large programs and populations.
                 Several variants of SGE employing different strategies
                 for controlling solution size and diversity are tested
                 on classic GP problems. Results compare favourably
                 against GP and newer techniques, with the best results
                 obtained by promoting diversity between derived
                 programs.",
}

Genetic Programming entries for Martin H Luerssen David M W Powers

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