Using Attribute Grammars for Genetic Representations of Neural Networks and
Syntactic Constraints of Genetic Programming
Talib S. Hussain and Roger A. Browse
Abstract
An attribute grammar (Knuth, 1968; Bochmann, 1976) is a context-free grammar augmented by the
assignment of semantic attributes to the symbols of the grammar. A production rule specifies
not only the replacement of symbols, but also the evaluation of the symbol's attributes. In
our research, an attribute grammar is used to specify classes of neural network structures with
explicit representation of their functional organization. These representations provide useful
constraints upon a genetic optimization that guarantee the preservation of syntactically correct
genetic trees with semantically meaningful sub-trees. In this paper, we give a broad overview
of our research into attribute grammar representations, from the basic and known capabilities,
to the current ideas being addressed, to the future directions of our research.