Hybrid memory scheme for genetic algorithm in dynamic environments

被引：0
|
作者：
Chen H. [1 ]
Li M. [2 ]
Chen X. [2 ]
机构：
[1] College of Automation Engineering, Nanjing University of Aeronautics and Astronautics
[2] Key Laboratory of Nondestructive Test under the Ministry of Education, Nanchang Hangkong University
来源：
Yingyong Kexue Xuebao/Journal of Applied Sciences | 2010年 / 28卷 / 05期
关键词：
Dynamic environment; Genetic algorithm; Memory scheme;
D O I：
10.3969/j.issn.0255-8297.2010.05.015
中图分类号：
学科分类号：
摘要：
In order to effectively solve dynamic optimization problems, a new hybrid memory scheme that consists of short-term memory and long-term memory is proposed. Information to be memorized includes the best individual and the probability vector of current population. Information of short-term memory is extracted to build the next population in each generation. Long-term memory is assigned for the short-term memory when a environmental change is detected. A new genetic algorithm is thus constructed based on the hybrid memory. Performance of the algorithm is verified in different environments including non-cyclic, cyclic, and cyclic with noise. Computation results indicate that this algorithm is superior to similar algorithms in dealing with dynamic optimization problems.
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页码：540 / 545
页数：5
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