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A Genetic Approach to Fusion of Algorithms for Compressive Sensing

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Advances in Neural Networks - ISNN 2017 (ISNN 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10262))

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Abstract

Inspired by the data fusion principle, we proposed a genetic approach to fusion of algorithms for CS to improve reconstruction performance. Firstly, several compressive sensing reconstruction algorithms (CSRAs) are executed in parallel to provide their estimates of the underlying sparse signal. Next, genetic algorithm is used to fuse these estimates for achieving a new estimate that is better than the best of these estimates. The proposed approach provides flexible design of fitness function and mutation strategy of genetic algorithm, and various participating CSRAs can be used to recover the sparse signal. Experiments were conducted on both synthetic and real world signals. Results indicate that the proposed approach has three advantages: (1) it performs well even when the dimension of measurements is very low, (2) reconstruction performance is better than any participating CSRAs, and (3) it is comparable or even superior to other fusion algorithm like FACS.

H. You—work was supported by National Natural Science Foundation of China (grant Nos. 61371147, and 11433002), and Shanghai Academy of Spaceflight Technology (grant No. SAST2015039).

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Authors and Affiliations

  1. Department of Electronic Engineering, Shanghai Jiao Tong University (SJTU), Shanghai, People’s Republic of China

    Hanxu You & Jie Zhu

Authors
  1. Hanxu You
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  2. Jie Zhu
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Correspondence to Hanxu You .

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Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Fengyu Cong

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

    Andrew Leung

  3. Chinese Academy of Sciences, Beijing, China

    Qinglai Wei

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You, H., Zhu, J. (2017). A Genetic Approach to Fusion of Algorithms for Compressive Sensing. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10262. Springer, Cham. https://doi.org/10.1007/978-3-319-59081-3_44

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  • DOI: https://doi.org/10.1007/978-3-319-59081-3_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59080-6

  • Online ISBN: 978-3-319-59081-3

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