Multi-strategy parallel genetic algorithm based on machine learning

被引：0

作者：

Zhang Y. ^{[1
]}

Zhong H. ^{[1
]}

Zhang C. ^{[1
]}

Li X. ^{[1
]}

Cong J. ^{[2
]}

机构：

[1] State Key Lab of Digital Manufacturing Equipment & Technology, Huazhong University of Science & Technology, Wuhan

[2] School of Mechanical Engineering, Shandong University of Technology, Zibo

来源：

Li, Xinyu (lixinyu@mail.hust.edu.cn) | 1600年 / CIMS卷 / 27期

关键词：

Genetic algorithms; K-means clustering algorithm; Machine learning; Parallel computing; Reinforcement learning;

D O I：

10.13196/j.cims.2021.10.016

中图分类号：

学科分类号：

摘要：

To improve the performance of genetic algorithm with machine learning method, a multi-strategy parallel genetic algorithm based on machine learning was proposed. The parallel thought was used to accelerate the evolutionary process of genetic algorithm, and the K-means clustering algorithm was applied to divide the initial population into multiple clusters. Then the similar individuals were evenly distributed to different subpopulations to ensure the diversity and uniformity of the subpopulations. In the process of evolution, the sub-populations were allowed to communicate with each other, and excellent individuals were used to replace poor individuals in other populations to improve the overall quality of the population. The reinforcement learning that could autonomously perceive the environment was introduced to realize the self-learning of the important parameter's crossover probability in genetic algorithm, so that the crossover probability adapted to the evolution process based on experience. The function experiment verified the superiority and stability of the multi-strategy parallel genetic algorithm based on machine learning. © 2021, Editorial Department of CIMS. All right reserved.

引用

页码：2921 / 2928

页数：7

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