A novel approach to Machine Discovery: Genetic Programming and Stochastic Grammars

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

@InProceedings{ILP02-Ratle,
  author =       "Alain Ratle and Michele Sebag",
  title =        "A novel approach to Machine Discovery: Genetic
                 Programming and Stochastic Grammars",
  booktitle =    "Proceedings of Twelfth International Conference on
                 Inductive Logic Programming",
  editor =       "S. Matwin and C. Sammut",
  year =         "2003",
  publisher =    "Springer Verlag",
  volume =       "2583",
  series =       "LNCS",
  pages =        "207--222",
  address =      "Sydney, Australia",
  month =        jul # " 9-11, 2002",
  keywords =     "genetic algorithms, genetic programming, ILP",
  ISBN =         "3-540-00567-6",
  URL =          "http://www.springerlink.com/openurl.asp?genre=issue&issn=0302-9743&volume=2583",
  size =         "http://www.lri.fr/~sebag/PS/ILP02.ps",
  abstract =     "The application of Genetic Programming (GP) to the
                 discovery of empirical laws most often suffers from two
                 limitations. The first one is the size of the search
                 space; the second one is the growth of non-coding
                 segments, the introns, which exhausts the memory
                 resources as GP evolution proceeds. These limitations
                 are addressed by combining Genetic Programming and
                 Stochastic Grammars. On one hand, grammars are used to
                 represent prior knowledge; for instance, context-free
                 grammars can be used to enforce the discovery of
                 dimensionally consistent laws, thereby significantly
                 restricting GP search space. On the other hand, in the
                 spirit of distribution estimation algorithms, the
                 grammar is enriched with derivation probabilities. By
                 exploiting such probabilities, GP avoids the intron
                 phenomenon. The approach is illustrated on a real-world
                 like problem, the identification of behavioral laws in
                 Mechanics.",
}

Genetic Programming entries for Alain Ratle Michele Sebag

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