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An Effective Genetic Algorithm with Uniform Crossover for Bi-objective Unconstrained Binary Quadratic Programming 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))

Abstract

The unconstrained binary quadratic programming problem is one of the most studied NP-hard problem with its various practical applications. In this paper, we propose an effective multi-objective genetic algorithm with uniform crossover for solving bi-objective unconstrained binary quadratic programming problem. In this algorithm, we integrate the uniform crossover within the hypervolume-based multi-objective optimization framework for further improvements. The computational studies on 10 benchmark instances reveal that the proposed algorithm is very effective in comparison with the original multi-objective optimization algorithms.

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Notes

  1. 1.

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Acknowledgments

The work in this paper was supported by the Fundamental Research Funds for the Central Universities (Grant No. A0920502051408-25), supported by the Research Foundation for International Young Scientists of China (Grant No. 61450110443), supported by the Scientific Research Foundation for the Returned Overseas Chinese Scholars (Grant No. 2015S03007) and supported by National Natural Science Foundation of China (Grant No. 61370150 and 71501157).

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan, People’s Republic of China

    Chao Huo & Rong-Qiang Zeng

  2. School of Mathematics, Southwest Jiaotong University, Chengdu, 610031, Sichuan, People’s Republic of China

    Rong-Qiang Zeng

  3. School of Management, Northwestern Polytechnical University, Xi’an, 710072, Shanxi, People’s Republic of China

    Yang Wang

  4. Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, People’s Republic of China

    Ming-Sheng Shang

Authors
  1. Chao Huo
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  2. Rong-Qiang Zeng
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  3. Yang Wang
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  4. Ming-Sheng Shang
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Corresponding author

Correspondence to Rong-Qiang Zeng .

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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Huo, C., Zeng, RQ., Wang, Y., Shang, MS. (2016). An Effective Genetic Algorithm with Uniform Crossover for Bi-objective Unconstrained Binary Quadratic Programming 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_7

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  • DOI: https://doi.org/10.1007/978-3-319-46257-8_7

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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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