适应值共享拥挤遗传算法

保持遗传算法在演化过程中的种群多样性，是将遗传算法成功应用于解决多峰优化问题和多目标优化问题的关键。适应值共享遗传算法和拥护遗传算法分别从不同角度改善了遗传算法的搜索能力，是寻找多个最优解的常用算法。将这两种算法的优点加以结合，提出适应值共享拥护遗传算法。数值测试结果表明，该算法比标准适应值共享遗传算法和确定性拥挤遗传算法具有更强的搜索能力。

Fitness Sharing Crowding Genetic Algorithm

By combining fitness sharing method and crowding method in selection stage and replacement stage of genetic algorithms respectively, a fitness sharing crowding genetic algorithms is proposed. It combines the advantages of both fitness sharing genetic algorithms and crowding genetic algorithms in searching ability. The suggested algorithm is proved to be more efficient in solving benchmark multimodal optimization problems than standard fitness sharing genetic algorithm and crowding genetic algorithm.