In this paper, an approach is presented to incorporate problem specific knowledge into a genetic algorithm which is used to compute near-optimum solutions to traveling salesman problems (TSP), The approach is based on using a tour construction heuristic for generating the initial population, a tour improvement heuristic for finding local optima in a given TSP search space, and new genetic operators for effectively searching the space of local optima in order to find the global optimum. The quality and efficiency of solutions obtained for a set of TSP instances containing between 318 and 1400 cities are presented.
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