Algorithm management using genetic search for computer-aided vehicle routing

Research into alternative ways of employing artificial intelligence techniques to direct mathematical algorithms is described. The authors have developed a software system called XVRP-GA that demonstrates an integrated framework for this synergism, in the domain of computer-aided vehicle routing and scheduling problems. The system assists researchers and decision makers in applying mathematical algorithms to a specific routing problem instance by intelligently adapting the algorithm to the problem description. The genetic search adaptively refines the parameters that control the work of the underlying algorithm. The resultant solutions are uniformly superior to the best known algorithms working alone. To reduce the computational overhead of genetic search, a mechanism for improving the performance of the search is employed. Several evaluation functions that permit the parallel investigation of multiple peaks in the search space are utilized, resulting in significantly increased efficiency in the genetic search