Including control architecture in attribute grammar
specifications of feedforward neural networks
Talib S. Hussain and Roger A. Browse
Abstract
An important problem in evolutionary computing is the design of genetic representations of neural networks that permit
optimization of topology and learning characteristics. One promising approach for genetic representation of neural networks is
the use of grammars to depict a process in which neural networks may be generated. Existing grammar representations of neural
networks describe classes of networks with homogenous processing elements, simple fixed learning mechanisms and little
organized topological structure. In previous research we have presented an attribute grammar representation for classes of
networks with modular topology. Each parse tree generated by the grammar encodes a neural network specification which is
subsequently executed by an interpreter. By expanding the grammar to include the control of the sequence of activity in the
networks, we have been able to reduce the interpreter to a simple model of the operation of individual neurons in the networks.
The expanded grammar may be used in an evolutionary computation to explore neural networks that vary in their architecture
and learning behaviors.