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

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Abstract

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. Additionally, many improvements have been proposed in the past two decades in order to allow the optimized SVM model to obtain better performance. This work focuses on reviewing the current state of genetic-based evolutionary algorithms used to optimize parameters of SVM and its variants. First, we introduce the principles of SVM and provide a survey on optimization methods of its parameters. Then we propose a taxonomy of improving genetic-based evolutionary algorithms according to code mechanism, parameters control, population structure, evolutionary strategy, operation mechanism, operators, and many other hybrid approaches. Furthermore, this paper analyzes and compares the advantages and disadvantages of the above algorithms explicitly, and provides their applicable scenarios as well. Finally, we highlight the existing problems of genetic-based evolutionary algorithms used for parameters optimization of SVM and prospect development trends of this field in the future.

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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).

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Authors and Affiliations

  1. School of Architectural and Surveying and Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China

    Weizhen Ji & Deer Liu

  2. School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan, 430074, China

    Yifei Meng

  3. School of Municipal and Surveying Engineering, Hunan City University, Yiyang, 413000, China

    Yun Xue

  4. Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Central South University, Changsha, 410012, China

    Yun Xue

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  1. Weizhen Ji
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  2. Deer Liu
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Correspondence to Deer Liu.

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Ji, W., Liu, D., Meng, Y. et al. A review of genetic-based evolutionary algorithms in SVM parameters optimization. Evol. Intel. 14, 1389–1414 (2021). https://doi.org/10.1007/s12065-020-00439-z

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