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A Hybrid Parameter Adaptation Based GA and Its Application for Data Clustering

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Bio-inspired Computing: Theories and Applications (BIC-TA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 791))

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Abstract

The performance of genetic algorithm (GA) critically depends on the rates of variation operation. In this paper, we propose a hybrid parameter adaptation scheme, which integrates the traditional adaptive and self-adaptive method, to dynamically control the crossover and mutation rate of GA during evolution. Such a scheme can take advantage of both adaptive and self-adaptive mechanisms, thus effectively setting the parameters of GA. The resulting GA has been applied for data clustering. Our results show that the proposed scheme is beneficial and the resulting GA outperforms the adaptive GA or self-adaptive GA for data clustering.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 61573316).

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Authors and Affiliations

  1. College of Computer Science, Zhejiang University of Technology, Hangzhou, 310023, Zhejiang, People’s Republic of China

    Kangfei Ye

  2. Department of Computer Science, Hangzhou Normal University, Hangzhou, 311121, Zhejiang, People’s Republic of China

    Weiguo Sheng

Authors
  1. Kangfei Ye
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  2. Weiguo Sheng
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Corresponding author

Correspondence to Weiguo Sheng .

Editor information

Editors and Affiliations

  1. School of Automation, Huazhong University of Science and Technology, Wuhan, China

    Cheng He

  2. Automation College, Harbin Engineering University, Harbin, China

    Hongwei Mo

  3. School of Automation, Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  4. Automation College, Harbin Engineering University, Harbin, China

    Yuxin Zhao

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Ye, K., Sheng, W. (2017). A Hybrid Parameter Adaptation Based GA and Its Application for Data Clustering. In: He, C., Mo, H., Pan, L., Zhao, Y. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2017. Communications in Computer and Information Science, vol 791. Springer, Singapore. https://doi.org/10.1007/978-981-10-7179-9_12

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  • DOI: https://doi.org/10.1007/978-981-10-7179-9_12

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7178-2

  • Online ISBN: 978-981-10-7179-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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