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Gene Sorting in Differential Evolution

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Advances in Neural Networks – ISNN 2009 (ISNN 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5553))

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Abstract

Gene sorting is a method proposed in this article that consists of ordering trial vector’s component in differential evolution (DE). This method tends to significantly increase the convergence speed of DE with just a little modification on the original algorithm. A benchmark set of 18 functions is used for comparing both algorithms. Most importantly, the proposed methods can be incorporated in other variants of DE to further increase their respective speeds; Iterated Function System Based Adaptive Differential Evolution (IFDE) is used in this paper as a variant example and it is about 5 times faster for 30-dimension problems.

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References

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Author information

Authors and Affiliations

  1. Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Remi Tassing, Desheng Wang, Yongli Yang & Guangxi Zhu

  2. Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China

    Guangxi Zhu

Authors
  1. Remi Tassing
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  2. Desheng Wang
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  3. Yongli Yang
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  4. Guangxi Zhu
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Editor information

Editors and Affiliations

  1. Departamento de Control Automático, CINVESTAV-IPN, A.P. 14-740, Av.IPN 2508, 07360, México D.F., México

    Wen Yu

  2. Deptartment of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, USA

    Haibo He

  3. Dept. of Electrical and Computer Engineering, South Dakota School of Mines & Technology, 501 E. St. Joseph Street, SD 57701, Rapid City, USA

    Nian Zhang

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© 2009 Springer-Verlag Berlin Heidelberg

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Tassing, R., Wang, D., Yang, Y., Zhu, G. (2009). Gene Sorting in Differential Evolution. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_73

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  • DOI: https://doi.org/10.1007/978-3-642-01513-7_73

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01512-0

  • Online ISBN: 978-3-642-01513-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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