Convergence of Genetic Evolution Algorithms for Optimization
Convergence of Genetic Evolution Algorithms for Optimization
复制标题
用于优化的遗传进化算法的收敛
DOI：
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发表时间：
1995
期刊：
影响因子：
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通讯作者：
Yong
中科院分区：
文献类型：
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作者：
Jun He;Lishan Kang;Yong
遗传算法是一种基于达尔文进化过程的高度并行、自适应的搜索方法。本文将遗传算法与模拟退火算法相结合，提出了一种新的随机搜索算法--遗传进化算法。给出了随机搜索算法以概率1收敛于全局最优解的条件，并利用马尔可夫链理论分析了遗传进化算法的收敛性。
Genetic algorithms are highly parallel, adaptive search method based on the processes of Darwinian evolution. This paper combines genetic algorithms with simulated annealing algorithms to a new kind of random search algorithms which is called genetic evolution algorithms. We give some conditions which guarantee random search algorithms to converge to the global optima set with probability 1 for solving optimization problems and analyze the convergence of genetic evolution algorithms by using Markov chain theory.