摘要
遗传算法是一种自适应启发式群体型选代式全局搜索算法，正受到许多学科的重视．本文首先以函数优化为例分析了遗传算法的运行过程，然后着重探讨了遗传算法的全局收敛性和效率问题，提出了有效基因的新概念及有效基因突变操作，推导出每次遗传搜索产生Ｏ（２ｌ－１）数量级的新模式，最后给出了结论．
Genetic algorithm(GA) is a kind of adaptive, heuristic, probabilistic and iterated searching algorithm, which has attracted many subjects. The architecture and running process of genetic algorithm are analyzed first as the example of function optimization in the paper, then, the global optimum and seaching efficiency are studied. The new concept of effective gene and effective gene mutation are proposed. The new schema produced during one.genetic operation are derived as O(2n-1). Finally, the conclusion is given.
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
1996年第3期297-304,共8页
Control Theory & Applications
基金
国家自然科学基金
上海市自然科学基金
关键词
遗传算法
收敛性
机器学习
人工智能
Genetic algorithm
global optimum
searching effciency
