With the growing interest in the practical use of neural networks, addressing the problem of customiling networks for specific applications is becoming increasingly critical. It has repeatedly been observed that different network structures and learning parameters can substantially affect performance.

With the growing interest in the practical use of neural networks, addressing the problem of customiling networks for specific applications is becoming increasingly critical. It has repeatedly been observed that different network structures and learning parameters can substantially affect performance.

With the growing interest in the practical use of neural networks, addressing the problem of customiling networks for specific applications is becoming increasingly critical.It has repeatedly been observed that different network structures and learning parameters can substantially affect performance. Such important aspects of neural network applications as generalilation, learning speed, connectivity andtolerance to network damage are strongly related to the choice of 448 Harp, Samad and Guha network architecture. Yet there are few analytic results, and few heuristics, that can help the application developer design an appropriate network. We have been investigating the use of the genetic algorithm (Goldberg, 1989; Holland, 1975) for designing application-specific neural networks (Harp, Samad and Guha, 1989ab). In our approach, the genetic algorithm is used to evolve appropriate network structures and values of learning parameters. In contrast, other recent applications of the genetic algorithm to neural networks (e.g., Davis [1988], Whitley [1988]) have largely restricted the role of the genetic algorithm to updating weights on a predetermined network structure-another logical approach.

Contrary to many prevailing approaches to knowledge acquisition, Laps, our expert-interviewing software, begins by soliciting cases from the expert, but it does not end there. Its uniqueness lies in the fact that it interweaves knowledge gathering, organizing, and testing. Laps begins with a case in the form of a sample solution path elicited from the domain expert. This sample solution path is refined by a process called dechunking, which facilitates finding a model of the expert's reasoning process. The model guides the determination of the structure of alternatives tables at an effective level of abstraction. Once these tables have been set up, the expert is able to produce row after row on his own until a complete rule base is built. A rule generator currently produces rules in Clips or M.1 syntax.