Application of multi-step GA to the traveling salesman problem

Although GAS are widely used for optimization problems and have obtained many good results, these are also holding problems, such as premature convergence problems and evolutionary stagnation problems. In this papel; the premature convergence caused by reduction of the diversity and evolutionary stagnation in GAS are observed, and a new genetic algorithm, named multi-step GA(MSGA) is proposed. MSGA introduces the ideas which is narrower the search space to avoid evolutionary stagnation and restarts from initial population with keeping past results to avoid premature convergence. To evaluate proposed MSGA, traveling salesman problems are applied. As a result, MSGA can avoid premature convergence and evolutionary stagnation, and shows higher performance than the other conventional GAS.