空军预警学院学报Issue(3)：208-212,5.DOI:10.3969/j.issn.2095-5839.2014.03.014
动态环境下基于马氏链预测和记忆机制的遗传算法
Genetic algorithm based on Markov chain prediction and memory mechanism in dynamic environment
陈莉 1张铁毅 2徐芳1
作者信息
- 1. 空军预警学院，武汉430019
- 2. 空军装备部，北京100038
- 折叠
摘要
Abstract
The population of traditional evolutionary algorithm would converge gradually and the diversity of population would be lost in the course of evolution. Once the environment changes and could not track fast the change’s optimum solution. To allow the evolutionary algorithm to deal with the optimal issue better in the dynamic environment, this paper presents a genetic algorithm (MMGA) that deals with dynamic optimal issue with limited discrete environment state changing. This proposed algorithm uses Markov chain to predict the probabilities of various environmental state occurring, and generates new population that is fit for the future environment by combining the optimal entity that is stored in memory and related to the environment with the allele distribution vectors. By experimenting on the test of dynamic optimization, it proves that the off-line performance of this proposed algorithm is superior to other three classical algorithms.关键词
遗传算法/动态优化/马氏链预测/记忆机制Key words
genetic algorithm/dynamic optimization/Markov chain prediction/memory mechanism分类
信息技术与安全科学引用本文复制引用
陈莉,张铁毅,徐芳..动态环境下基于马氏链预测和记忆机制的遗传算法[J].空军预警学院学报,2014,(3):208-212,5.
