A solution of combinatorial optimization problem by uniting genetic algorithms with Hopfield's model

It is important to solve a combinatorial optimization problem because of its utility. In this paper, the authors propose a method of solving combinatorial optimization problems by uniting genetic algorithms (GAs) with Hopfield's model (Hp model). The authors also apply it to the traveling salesman problem (TSP). GAs are global search algorithms. On the other hand, in the Hp model the range of a search is in the neighborhood of the initial point. Then the Hp model is local search algorithm. By using these natures that make up for defects of each other, the authors unite GAs with the Hp model. Then the authors can overcome some difficulties, such as coding and crossover in GAs and setting up the initial point and parameter in the Hp model. The availability of the authors' proposed approach is verified by simulations.<
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