This paper examines the feasibility of using genetic algorithms augmented with a long term memory to attack similar traveling salesman problems. The proposed learning system combines a genetic problem solver with a case-base or data-base of past problem solving attempts to increase performance with experience. Instead of starting from scratch, we retrieve and inject the solutions of previously solved similar problems into the initial population of the genetic algorithm to provide a performance boost. In this paper we are more concerned with relative improvement in performance over time rather than with absolute performance and our results on a number of problems indicate that we can always get better performance with the combined system. If, as we believe, the results are generalizable, combining a case-base with a genetic algorithm will benefit most purely genetic algorithm implementations.