摘要
针对基本遗传算法SGA在搜索过程中易陷入局部最优解的问题,提出了基于熵测度的自适应遗传算法,并分析了熵测度下种群个体被选概率的极限行为。理论分析和对比实验表明,基于熵测度的自适应选择策略能根据种群性状来动态地调整选择压力,从而调整算法的开采和探索能力的平衡,提高算法的全局优化性能。
The basic operation methods and correlative parameters of genetic algorithm indicate the balance between the exploitation and exploration, but the simple genetic algorithm SGA easily gets into local optimal solution in the process of searching. The authors propose an adaptive genetic algorithm based on entropy measurement, and deduce the limit of the selection probabilities of individuals under entropy measurement. The theoretical analysis and a comparative experiment show that the new selection strategy based on entropy measurement can adjust dynamically the selection intensity according to the population state, which improves the global optimal performance of the algorithm.
出处
《西华大学学报（自然科学版）》
CAS
2013年第3期45-49,84,共6页
Journal of Xihua University:Natural Science Edition
基金
国家自然科学基金(11161041)
中央高校中青年科研基金项目(ZYJ2012004)
西北民族大学中青年科研基金项目(X2009-012)
关键词
遗传算法
自适应
熵
未成熟收敛
genetic algorithm
self-adaptive
entropy
premature convergence
