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International Conference on Genetic and Evolutionary Computing

ICGEC 2017: Genetic and Evolutionary Computing pp 3–11Cite as

A Many-Objective Evolutionary Algorithm with Reference Point-Based and Vector Angle-Based Selection

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 579))

Abstract

In this paper we proposed a many-objective evolutionary algorithm by combining the reference point-based selection in NSGA-III and the vector angle-based selection in VaEA. Performance of the proposed algorithm is verified by testing on the negative version of four DTLZ functions. The proposed algorithm is better than NSGA-III and is comparable to VaEA in terms of IGD. Besides, the proposed algorithm is more robust and can expand the front better.

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References

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Acknowledgement

This research was supported by the Ministry of Science and Technology of Taiwan (R.O.C.) under Grant No. 105-2221-E-003-021.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Taiwan Normal University, Taipei, Taiwan (R.O.C.)

    Chen-Yu Lee, Jia-Fong Yeh & Tsung-Che Chiang

Authors
  1. Chen-Yu Lee
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  2. Jia-Fong Yeh
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  3. Tsung-Che Chiang
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Corresponding author

Correspondence to Tsung-Che Chiang .

Editor information

Editors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China

    Jerry Chun-Wei Lin

  2. Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, Fujian, China

    Jeng-Shyang Pan

  3. School of Computer Science, Engineering and Mathematics, Flinders University, Bedford Park, South Australia, Australia

    Shu-Chuan Chu

  4. School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China

    Chien-Ming Chen

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Lee, CY., Yeh, JF., Chiang, TC. (2018). A Many-Objective Evolutionary Algorithm with Reference Point-Based and Vector Angle-Based Selection. In: Lin, JW., Pan, JS., Chu, SC., Chen, CM. (eds) Genetic and Evolutionary Computing. ICGEC 2017. Advances in Intelligent Systems and Computing, vol 579. Springer, Singapore. https://doi.org/10.1007/978-981-10-6487-6_1

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  • DOI: https://doi.org/10.1007/978-981-10-6487-6_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6486-9

  • Online ISBN: 978-981-10-6487-6

  • eBook Packages: EngineeringEngineering (R0)

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