摘要
This paper describes evolution strategy procedures for real-valued function optimization for the purpose of analyzing its asymptotic convergence properties. Two convergence theorems, which show that evolution strategy asymptotically converges to a global minmize point with probability one, are given.
This paper describes evolution strategy procedures for real-valued function optimization for the purpose of analyzing its asymptotic convergence properties. Two convergence theorems, which show that evolution strategy asymptotically converges to a global minmize point with probability one, are given.
出处
《计算数学》
CSCD
北大核心
2001年第1期105-110,共6页
Mathematica Numerica Sinica
基金
国家自然科学基金
关键词
进化算法
演化策略
最优化
全局收敛性
遗传算法
evolutionary algorithms, evolution strategies, optimization, global convergence properties
