title: Genetic algorithms for route discovery.
abstract: 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.
authors.affiliation: Department of Electrical and Electronic Engineering, Intelligent Systems and Networks (ISN), Imperial College, SW7 2BT London, UK. e.gelenbe@imperial.ac.uk
authors.forename: Erol
authors.initials: E
authors.lastname: Gelenbe
authors.forename: Peixiang
authors.initials: P
authors.lastname: Liu
authors.forename: Jeremy
authors.initials: J
authors.lastname: Lainé
mesh_terms: D000465:Algorithms; D003071:Cognition; D003195:Computer Communication Networks; D003198:Computer Simulation; D005823:Genetics