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A review of genetic-based evolutionary algorithms in SVM parameters optimization

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成果类型:
期刊论文
作者:
Ji, Weizhen;Liu, Deer;Meng, Yifei;Xue, Yun
通讯作者:
Liu, Deer(landserver@163.com)
作者机构:
[Ji, Weizhen; Liu, Deer] School of Architectural and Surveying and Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou
341000, China
[Meng, Yifei] School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan
430074, China
[Xue, Yun] School of Municipal and Surveying Engineering, Hunan City University, Yiyang
通讯机构:
[Deer Liu] S
School of Architectural and Surveying and Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou, China
语种:
英文
关键词:
Differential evolution algorithm;Evolutionary algorithms;Genetic algorithm;Global optimization;Immune algorithm;Parameter optimization;Support vector machine
期刊:
Evolutionary Intelligence
ISSN:
1864-5909
年:
2021
卷:
14
期:
4
页码:
1389-1414
基金类别:
The funding was provided by National Natural Science Foundation of China (Grand Nos. 41361077, 41561085) and Natural Science Foundation of Jiangxi Province (Grand No. 20161BAB203091).
机构署名:
本校为其他机构
院系归属:
市政与测绘工程学院
摘要:
Parameters optimization is a research hotspot of SVM and has gained increasing interest from various research fields. Compared with other optimization algorithms, genetic-based evolutionary algorithms that have achieved optimization according to the laws of separation and free combination in genetics are gradually attracted much attention. Also, due to the characteristics of self-organization and self-adaptation, these algorithms often enable SVM to obtain appropriate parameters, so that the model can be applied to more applications. Additional...

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