抄録
The genetic algorithm (GA) is an optimization technique simulating the process of natural evolution, and it has been successfully applied to several optimization problems which are difficult to solve exactly by conventional methods. This paper proposes a new method for solving the traveling salesman problem (TSP) based on the GA. In applications of GA to TSP proposed so far, a coding where the chromosome represents a list of cities arrayed in the visiting order has been mainly used. However, in such a coding, we have to devise a crossover operator that keeps each chromosome to be a permutation, and it inevitably causes a difficulty in inheritance of tour characteristics.
The present paper proposes a new method in which a genetic coding represents edges of the tour, and a crossover operator exchanges the edges of the parent tours. The effectiveness of the proposed method is confirmed through several computational experiments, including a comparison with another typical method. Furthermore, the paper proposes an algorithm which combines GA with the 2-opt method, a local search technique. The effectiveness of this algorithm is also confirmed through a comparison with other methods for solving the TSP.
