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Multitask Feature Selection for Objective Reduction

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Evolutionary Multi-Criterion Optimization (EMO 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12654))

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Abstract

Objective reduction has been regarded as a major tool for solving many-objective optimization problems (MaOPs). This paper proposes a multitask feature selection method for objective reduction. In our proposed method, each objective is formulated as a positive linear combination of a small number of essential objectives, and sparse regularization is employed to identify redundant objectives. Our numerical experiment shows the effectiveness and robustness of the proposed method by comparing it with some state-of-the-art objective reduction methods.

This work was supported by the National Natural Science Foundation of China (Grant No: 61876163) and ANR/RGC Joint Research Scheme sponsored by the Research Grants Council of the Hong Kong Special Administrative Region, China and France National Research Agency (Project No: A-CityU101/16).

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Author information

Authors and Affiliations

  1. Department of Computer Science, City University of Hong Kong, Hong Kong, China

    Genghui Li & Qingfu Zhang

  2. The City University of Hong Kong Shenzhen Research Institute, Shenzhen, China

    Genghui Li & Qingfu Zhang

Authors
  1. Genghui Li
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  2. Qingfu Zhang
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Corresponding author

Correspondence to Genghui Li .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Hisao Ishibuchi

  2. City University of Hong Kong, Kowloon Tong, China

    Qingfu Zhang

  3. Southern University of Science and Technology, Shenzhen, China

    Ran Cheng

  4. University of Exeter, Exeter, UK

    Ke Li

  5. Xi'an Jiaotong University, Xi'an, China

    Hui Li

  6. Xidian University, Xi'an, China

    Handing Wang

  7. East China Normal University, Shanghai, China

    Aimin Zhou

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Li, G., Zhang, Q. (2021). Multitask Feature Selection for Objective Reduction. In: Ishibuchi, H., et al. Evolutionary Multi-Criterion Optimization. EMO 2021. Lecture Notes in Computer Science(), vol 12654. Springer, Cham. https://doi.org/10.1007/978-3-030-72062-9_7

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  • DOI: https://doi.org/10.1007/978-3-030-72062-9_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72061-2

  • Online ISBN: 978-3-030-72062-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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