Using attribute grammars for the genetic selection of backpropagation networks for character recognition

Roger A. Browse, Talib S. Hussain and Matthew B. Smillie

Abstract

Determining exactly which neural network architecture, with which parameters, will provide the best solution to a classification task is often based upon the intuitions and experience of the implementers of neural network solutions. The research presented in this paper is centered on the development of automated methods for the selection of appropriate networks, as applied to character recognition. The Network Generating Attribute Grammar Encoding (NGAGE) system is a compact and general method for the specification of commonly accepted network architectures that can be easily expanded to include novel architectures, or that can be easily restricted to a small subset of some known architecture. Within this system, the context-free component of the attribute grammar specifies a class of basic architectures by using the non-terminals to represent network layers and component structures. The inherited and synthesized attributes indicate the connections necessary to develop a functioning network from any parse tree that is generated from the grammar. The attribute grammar encoding is particularly conducive to the use of genetic algorithms as a strategy for searching the space of possible networks. The resultant parse trees are used as the genetic code, permitting a variety of different genetic manipulations. We apply this approach in the generation of backpropagation networks for recognition of characters from a set consisting of 20,000 examples of 26 letters.