Genetic algorithms for solving shortest path problems

Abstract

In this study, we investigated the possibility of using genetic algorithms to solve shortest path problems. The most thorny and critical task for developing a genetic algorithm to this problem is how to encode a path in a graph into a chromosome. A priority-based encoding method is proposed which can potentially represent all possible paths in a graph. Because a variety of network optimization problems may be solved, either exactly or approximately, by identifying shortest path, this studies will provide a base for constructing efficient solution procedures for shortest path-based network optimization problems. The proposed approach has been tested on three randomly generated problems with different size from 6 nodes to 70 nodes and from 10 edges to 211 edges. The experiment results are very encouraging: it can find the known optimum very rapidly with very high probability. It can be believed that genetic algorithms may hopefully be a new approach for such kinds of difficult-to-solve problems.