摘要
根据种子到当前最优点的距离将种群分成两部分,小于或等于某一自适应距离值的种子归入当前最优种群,大于该距离值的次优种子形成次优种群集合。对此两个种群分别按照不同的进化策略协同进化并重组。通过界定最优种群边界来提高遗传算法局部搜索能力,通过对次优种群自适应变异,比较好地平衡种群的"选择压力"和"种群多样性"。数值结果表明了本方法的有效性和稳定性。
A novel genetic algorithm with several elitists preserved was proposed. The population was divided into two parts according to the distances between individuals and the current individual. One was the optimal population of current generation, and the other aggregated hypo-opt individuals. This method can enhance local searching performance by bounding constrained optimal population and can raise the population diversity by introducing self-adaptive mutation probability in hypoopt population. Some numerical tests have been made and the results show that the algorithm is effective.
出处
《计算机应用》
CSCD
北大核心
2008年第4期939-941,共3页
journal of Computer Applications
基金
国家973规划项目(2002CB312200)
关键词
遗传算法
多精英保存
种群划分
进化策略
自适应变异
genetic algorithms
multi-elitist preservation
population division
evolution strategy
adaptive mutation
作者简介
朱灿（1973-），男，湖南双峰人，讲师，博士研究生，主要研究方向：演化算法；zhucanplus@163．com
梁昔明（1967-），男，湖南汨罗人，教授，博士生导师，主要研究方向：工业过程稳态优化控制、大规模优化理论与算法。
