Improvement of niching genetic algorithms using crowding
排挤小生态遗传算法的改进方法

A class of niching genetic algorithms using clustering crowding is proposed.By analyzing topology of fitness landscape and extending the space for searching similar individual,clustering crowding can determine the locality of search space more accurately,thus decreasing the replacement errors of crowding and suppressing genetic drift of the population.The integration of deterministic and probabilistic crowding increases the capacity of both parallel local hill_climbing and maintaining multiple subpopulations.The experimental results optimizing various multimodal functions show that,the performances such as the number of effective peaks,average peak ratio and global optimum ratio of genetic algorithms using clustering crowding are uniformly superior to that of the genetic algorithms using fitness sharing,simple deterministic crowding and probabilistic crowding.