Exploring Self-Adaptive Methods to Improve the Efficiency of Generating Approximate Solutions to Travelling Salesman Problems Using Evolutionary Programming

Self-adaptation is becoming a standard method for optimizing mutational parameters within evolutionary programming. The majority of these efforts have been applied to continuous optimization problems. This paper offers a preliminary investigation into the use of self-adaptation for discrete optimization using the traveling salesman problem. Two self-adaptive approaches are analyzed. The results indicate that the use of self-adaptation can yield statistically significantly improved solutions over the failure to use any self-adaptation at all. This improvement comes at the expense of greater computational effort.