Route Planning with Ga

In previous work we proposed a new evolutionary algorithm , GA , which incorporates features of both the classical search algorithm A and genetic algorithms. In this paper we describe the application of GA to a hard opti-misation problem, route planning in complex terrains for Computer Generated Forces (CGF). We report the performance of the algorithm on a large number of route-planning problems and compare its performance with that of a standard GA and classical search techniques. Our results indicate that the plans produced by GA are comparable in cost with those produced by a standard GA but require an order of magnitude fewer tness evaluations, resulting in a signiicant speedup.