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TSP is a well-known NP-hard problem. Although many algorithms for solving TSP, such as linear programming, dynamic programming, genetic algorithm, anneal algorithm, and ACO algorithm have been proven to be effective, they are not so suitable for the more complicated large scale TSP. This paper offers a method to decompose the large-scale data into several small-scale data sets by its relativity; and the results of each small-scale data set which represents a small-scale TSP compose the whole result of the large-scale TSP. An adaptive clustering method is presented and a novel genetic algorithm for TSP is described in this paper.