Cellular encoding as a graph grammar   [CE]

by

Gruau, F.

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Info: IEE Colloquium on Grammatical Inference: Theory, Applications and Alternatives (Journal), 1993, p. 17/1-10
Keywords:genetic algorithm connectionism neural networks cogann
Abstract:
ABSTRACT Cellular encoding [CE] is a method for encoding a family of neural networks [NN] into a set of labeled trees. Such sets of trees can be evolved by the genetic algorithm [GA] so as to find a particular set of trees that encodes a family of Boolean neural networks [NN] for computing a family of Boolean functions. Cellular encoding [CE] is presented as a graph grammar. A method is proposed for translating a cellular encoding [CE] into a set of graph grammar rewriting rules of the kind used in the Berlin algebraic approach to graph rewriting. The genetic search of neural networks [NN] via cellular encoding [CE] appears as a grammatical inference process [GI] where the language to parse is implicitly specified, instead of explicitly by positive and negative examples. Experimental results shows that the genetic algorithm [GA] can infer grammars that derive neural networks [NN] for the parity, symmetry and decoder Boolean function of arbitrary large size.
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BibTex:
@Article{Gruau93,
  author =       "Frederic Gruau",
  editor =       "Simon Lucas",
  title =        "Cellular encoding as a graph grammar",
  journal =      "IEE Colloquium on Grammatical Inference: Theory,
                 Applications and Alternatives",
  volume =       "(Digest No.092)",
  pages =        "17/1--10",
  publisher =    "IEE",
  address =      "London",
  month =        "22-23 " # apr,
  year =         "1993",
  keywords =     "genetic algorithm connectionism neural networks
                 cogann",
  abstract =     "ABSTRACT Cellular encoding is a method for encoding a
                 family of neural networks into a set of labeled trees.
                 Such sets of trees can be evolved by the genetic
                 algorithm so as to find a particular set of trees that
                 encodes a family of Boolean neural networks for
                 computing a family of Boolean functions. Cellular
                 encoding is presented as a graph grammar. A method is
                 proposed for translating a cellular encoding into a set
                 of graph grammar rewriting rules of the kind used in
                 the Berlin algebraic approach to graph rewriting. The
                 genetic search of neural networks via cellular encoding
                 appears as a grammatical inference process where the
                 language to parse is implicitly specified, instead of
                 explicitly by positive and negative examples.
                 Experimental results shows that the genetic algorithm
                 can infer grammars that derive neural networks for the
                 parity, symmetry and decoder Boolean function of
                 arbitrary large size.",
}