On the power of evolutionary optimization at the example of ATSP and large TSP Problems

The hybridization of both, genetic algorithms and local search methods, has been shown to be an eeective route to follow for nding high quality solutions for hard optimization problems. Previously proposed genetic local search approaches for the traveling salesman problem focused on the importance of using a high quality local search method in order to yield high quality solutions. This paper presents an alternative approach. Instead of investigating in the development and implementation of complex application speciic local search method we use a simple local search method and investigate in the evolutionary search. Besides the evolutionary factors of selection, mutation, and recombination used traditionally we make intense use of the fourth evolutionary factor identiied by Darwin: the isolation. The paper focus on the question what evolution can contribute to yield high quality solutions at hand of both, the symmetric and the asymmetric traveling salesman problem.