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Packet routing in networks requires knowledge about available paths, which can be either acquired dynamically while the traffic is being forwarded, or statically (in advance) based on prior information of a network's topology. This paper describes an experimental investigation of path discovery using genetic algorithms (GAs). We start with the quality-of-service (QoS)-driven routing protocol called ‘cognitive packet network’ (CPN), which uses smart packets (SPs) to dynamically select routes in a distributed autonomic manner based on a user's QoS requirements. We extend it by introducing a GA at the source routers, which modifies and filters the paths discovered by the CPN. The GA can combine the paths that were previously discovered to create new untested but valid source-to-destination paths, which are then selected on the basis of their ‘fitness.’ We present an implementation of this approach, where the GA runs in background mode so as not to overload the ingress routers. Measurements conducted on a network test bed indicate that when the background-traffic load of the network is light to medium, the GA can result in improved QoS. When the background-traffic load is high, it appears that the use of the GA may be detrimental to the QoS experienced by users as compared to CPN routing because the GA uses less timely state information in its decision making. [ABSTRACT FROM AUTHOR]

This guest editorial introduces a special issue of the periodical "IEEE Transactions on Software Engineering" which focuses on neural network software and systems. It outlines the history of artificial neural networks and their role in the origin of the theory of computing. The special issue addresses aspects of neural computation that are closely related to software engineering. The main problem at hand is the development of paradigms to deal effectively with the issues related to neural network programming. The papers in the issue highlight increasingly important issues of artificial neural network programming tools and systems, and suggest applications of neural networks to the diverse and difficult problems of software engineering.