Application of multi-step GA to the traveling salesman problem
Hirokazu Watabe, Tsukasa Kawaoka · 2002
Although GAs are widely used for optimization problems and often produce good results, there are also problems, such as premature convergence and evolutionary stagnation. The premature convergence caused by a reduction of the diversity and evolutionary stagnation in GAs are observed, and a new genetic algorithm, named multi-step GA (MSGA) is proposed. MSGA narrows the search space to avoid evolutionary stagnation and restarts from the initial population, keeping past results to avoid premature convergence. To evaluate MSGA, traveling salesman problems are considered. As a result, MSGA can avoid premature convergence and evolutionary stagnation and shows higher performance than other conventional GAs.