@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 = "17/110",
publisher = "IEE",
address = "London",
month = "2223 " # 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.",
}
