多様性を段階毎に回復する遺伝的アルゴリズムの提案と評価

抄録

Although GAs are widely used for optimization problems and have obtained many good results, these are also holding problems, such as premature convergence problems. In this paper, the premature convergence problems caused by reduction of the diversity in GAs are observed, and a new genetic algorithm, named multi-step GA (MSGA) is proposed. With MSGA, the diversity can be recovered for every certain segment of generations with keeping the evolution result till then. MSGA is the algorithm using hill climbing method as frame work, which starts some phenotype as an initial value and changes the value to the better direction. To evaluate proposed MSGA, function optimization problems and shape design problems are applied. As a result, MSGA can avoid the premature convergence and shows higher performance than the other conventional GAs for some problems.