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Gray-Coded Clonal Selection Algorithm for Optimization Problem

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Intelligent Data Engineering and Automated Learning – IDEAL 2016 (IDEAL 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9937))

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Abstract

Clonal Selection Algorithm (CSA), inspired by the clonal selection theory, has gained much attention and wide applications. In most common forms, the CSAs use a binary representation of variables, and the emulated immune operators, mutation, proliferation, selection, for example, are made to act on it. However, the binary representation often suffers from the so-called Hamming Cliff problem. In order to overcome this problem, a Gray-coded CSA is presented and used to solve optimization problems. The algorithm is applied to numerous bench-mark problems of numerical optimization problems and the computational results show effectiveness of the proposed algorithm.

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Acknowledgements

This work was supported by the Prospective Joint Research of University-Industry Cooperation of Jiangsu (No. BY2015248, BY2016056-02), the Six Talent Peaks Project of Jiangsu (No. XXRJ-013), Lianyungang Science and Technology Project (No. CG1413, CG1501), and the Natural Science Foundation of Huaihai Institute of Technology (No. z2015005, z2015012).

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Authors and Affiliations

  1. School of Computer Engineering, Huaihai Institute of Technology, Lianyungang, 222005, Jiangsu, China

    Hongwei Dai & Yu Yang

Authors
  1. Hongwei Dai
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  2. Yu Yang
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Corresponding author

Correspondence to Hongwei Dai .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, United Kingdom

    Hujun Yin

  2. Nanjing University, Nanjing, China

    Yang Gao

  3. Yangzhou University, Yangzhou, Jiangsu, China

    Bin Li

  4. Aeronautics and Astronautics, Nanjing University Aeronautics and Astronautics, Nanjing, China

    Daoqiang Zhang

  5. Nanjing Normal University, Nanjing, China

    Ming Yang

  6. Yangzhou University, Yangzhou, Jiangsu, China

    Yun Li

  7. Ostfalia University of Applied Sciences, Wolfenbüttel, Germany

    Frank Klawonn

  8. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

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© 2016 Springer International Publishing AG

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Dai, H., Yang, Y. (2016). Gray-Coded Clonal Selection Algorithm for Optimization Problem. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2016. IDEAL 2016. Lecture Notes in Computer Science(), vol 9937. Springer, Cham. https://doi.org/10.1007/978-3-319-46257-8_29

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  • DOI: https://doi.org/10.1007/978-3-319-46257-8_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46256-1

  • Online ISBN: 978-3-319-46257-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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