Evolutionary Pattern Matching Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Nadia Nedjah and Luiza {de Macedo Mourelle}",
  title =        "Evolutionary Pattern Matching Using Genetic
  year =         "2006",
  booktitle =    "Genetic Systems Programming: Theory and Experiences",
  pages =        "81--104",
  volume =       "13",
  series =       "Studies in Computational Intelligence",
  editor =       "Nadia Nedjah and Ajith Abraham and 
                 Luiza {de Macedo Mourelle}",
  publisher =    "Springer",
  address =      "Germany",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-29849-5",
  DOI =          "doi:10.1007/3-540-32498-4_4",
  abstract =     "In this chapter, we presented a novel approach to
                 generate adaptive matching automata for non-sequential
                 pattern set using genetic programming. we first defined
                 some notation and necessary terminologies. Then, we
                 formulated the problem of pattern matching and the
                 impact that the traversal order of the patterns has on
                 the process efficiency, when the patterns are
                 ambiguous. We also gave some heuristics that allow the
                 engineering of a relatively good traversal order. In
                 the main part of the chapter, we described the
                 evolutionary approach that permits the discovery of
                 traversal orders using genetic programming for a given
                 pattern set. For this purpose, we presented how the
                 encoding of traversal orders is done and consequently
                 how the decoding of an evolved traversal order into the
                 corresponding adaptive pattern-matcher. We also
                 developed the necessary genetic operators and showed
                 how the fitness of evolved traversal orders is
                 computed. We evaluated how sound is the obtained
                 traversal. The optimisation was based on three main
                 characteristics for matching automata, which are
                 termination, code size and required matching time.
                 Finally, we compared evolutionary adaptive matching
                 automata, obtained for some universal benchmarks, to
                 their counterparts that were designed using classic
  notes =        "http://www.springer.com/sgw/cda/frontpage/0,11855,5-146-22-92733168-0,00.html",

Genetic Programming entries for Nadia Nedjah Luiza de Macedo Mourelle