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Hybrid Crossover Based Clonal Selection Algorithm and Its Applications

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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))

Abstract

Hybridization is confirmed as an effective way of combining the best properties of different algorithms and achieving better performances. A framework of hybrid crossover is constructed and combined with clonal selection algorithm (CSA). The new crossover solutions are generated by the mutual influence of both high affinity and low affinity solutions. Simulation results based on the traveling salesman problems demonstrate the effectiveness of the hybridization.

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Acknowledgements

This work was supported by the Prospective Joint Research of University-Industry Cooperation of Jiangsu (No. BY2016056-02, BY2015248), 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 & Cunhua Li

Authors
  1. Hongwei Dai
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  2. Yu Yang
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  3. Cunhua Li
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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., Li, C. (2016). Hybrid Crossover Based Clonal Selection Algorithm and Its Applications. 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_50

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  • DOI: https://doi.org/10.1007/978-3-319-46257-8_50

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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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