Self-learning genetic algorithm

被引：0
|
作者：
V. A. Kostenko
A. V. Frolov
机构：
[1] Moscow State University,
来源：
Journal of Computer and Systems Sciences International | 2015年 / 54卷
关键词：
Genetic Algorithm; Fitness Function; System Science International; Probability Matrice; Stop Condition;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, a self-learning genetic algorithm for solving combinatorial optimization problems is considered. The self-learning consists in changing the values of the probabilities of crossing and mutation in accordance with changing the value of the fitness function after operations in the next iteration of the algorithm. The results of comparing the proposed algorithm with the Holland algorithm by the problems of multiprocessor job scheduling and subset sum problem are presented.
引用
收藏
页码：525 / 539
页数：14
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