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.

(CGE), a new evolutionary automatic programming algorithm that extends standard grammar evolution (GE) by replacing context-free grammars by Christiansen grammars. GE only takes into account syntactic restrictions to generate valid individuals. CGE adds semantics to ensure that both semantically and syntactically valid individuals are generated. It is empirically shown that our approach improves GE performance and even allows the solution of some problems are difficult to tackle by GE. Index Terms—Automatic programming, formal languages, genetic algorithms (GAs), languages. I.

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 ...

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 ne...