Grammar-guided genetic programming and dimensional consistency: application to non-parametric identification in mechanics

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

@Article{Ratle:2001:ASC,
  author =       "Alain Ratle and Michele Sebag",
  title =        "Grammar-guided genetic programming and dimensional
                 consistency: application to non-parametric
                 identification in mechanics",
  journal =      "Applied Soft Computing",
  volume =       "1",
  pages =        "105--118",
  year =         "2001",
  number =       "1",
  keywords =     "genetic algorithms, genetic programming, context-free
                 grammars",
  URL =          "http://www.sciencedirect.com/science/article/B6W86-43S6W98-B/1/38e0fa6ac503a5ef310e2287be01eff8",
  DOI =          "doi:10.1016/S1568-4946(01)00009-6",
  abstract =     "Although genetic programming has often successfully
                 been applied to non-parametric modeling, it is
                 frequently impaired by the huge size of the search
                 space explored. Domain knowledge is a powerful way to
                 trim out the size of the space, by restricting the
                 search to a priori relevant models. A most natural
                 domain knowledge in scientific modeling is known as
                 dimensional analysis, stipulating that the models must
                 be consistent with regards to the variable measurement
                 units.In this paper, it is shown that dimensional
                 analysis can automatically be expressed as a context
                 free grammar. Dimensionally-aware GP is thus achieved
                 by employing the dimensional grammar within the
                 grammar-guided GP framework first investigated by Gruau
                 [On using syntactic constraints with genetic
                 programming, in: P. Angeline, K.E. Kinnear Jr. (Eds.),
                 Advances in Genetic Programming II, MIT Press,
                 Cambridge, MA, 1996, pp. 377-394.]. However,
                 grammar-guided genetic programming encounters severe
                 difficulties when it involves a complex grammar, which
                 might explain why this approach has not been widely
                 used so far. The drawback is blamed on the
                 initialization step, which hardly constructs a
                 sufficiently diversified initial population, thus
                 hindering the success of evolution. This limitation is
                 addressed by a new CFG compliant initialization
                 procedure.The approach is validated on two problems
                 related to the identification of mechanical properties
                 of materials.",
}

Genetic Programming entries for Alain Ratle Michele Sebag

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