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Gene sorting in differential evolution with cross-generation mutation

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Abstract

Gene sorting is proposed in this paper as a method 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. In the meantime, a new concept of cross-generation mutation is introduced in order to perform the evolution process serially rather than parallelly. When combined with gene sorting, this method will further increase the convergence speed. A benchmark set of 18 functions is used to investigate the performance of these algorithms. Most importantly, the proposed methods can be incorporated in other variants of DE to further increase their respective speeds. Three versions of self-adaptive DE, namely iterated function system based adaptive differential evolution (IFDE), Janez’s DE (jDE) and SaDE, are taken as examples, which are averagely 10, 6 and 4 times faster than the benchmark set respectively.

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References

  1. Storn R, Price K. Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim, 1997, 11: 341–359

    Article  MATH  MathSciNet  Google Scholar 

  2. Feoktistov V. Differential Evolution: In Search of Solutions. Vol. 5. New York: Springer, 2006

    Google Scholar 

  3. Liu J, Lampinen J. A fuzzy adaptive differential evolution algorithm. In: Proceedings of IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering. Beijing, China, 2002. 606–611

  4. Qin A K, Huang V L, Suganthan P N. Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput, 2009, 13: 398–417

    Article  Google Scholar 

  5. Rahnamayan S, Tizhoosh H R, Salama M M A. Opposition-based differential Evolution. IEEE Trans Evol Comput, 2008, 12: 64–79

    Article  Google Scholar 

  6. Hardy G H, Littlewood J E, Pólya G. Inequalities. London: Cambridge University Press, 1934

    Google Scholar 

  7. Storn R. Differential evolution design of an IIR-filter. In: Proceedings of IEEE International Conference on Evolutionary Computation, Nayoya University, Japan, 1996. 268–273

    Chapter  Google Scholar 

  8. Coelho L S, Mariani V C. Combining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve-point effect. IEEE Trans Power Syst, 2006, 21: 989–996

    Article  Google Scholar 

  9. Liang C H, Chung C Y, Wong K P, et al. Study of differential evolution for optimal reactive power flow. IET Gen Trans Distrib, 2007, 1: 253–260

    Article  Google Scholar 

  10. Weitkemper P, Zielinski K, Kammeyer K D, et al. Optimization of interference cancellation in coded CDMA systems by means of differential evolution. In: 4th International Symposium on Turbo Codes & Related Topics. Munich, 2006. 1–6

  11. Tassing R, Wang D S, Yang Y L, et al. Gene sorting in differential evolution. In: 6th International Symposium on Neural Networks. Wuhan, China, 2009

  12. Li Y L, Ding F, Wang Y X. Iterated function system based adaptive differential evolution algorithm. In: Proceedings of IEEE Congress on Evolutionary Computation. Hong Kong, 2008. 1290–1294

  13. Brest J, Bošković B, Greiner S, et al. Performance comparison of self-adaptive and adaptive differential evolution algorithms. Soft Comput Fusion Found Meth Appl, 2007, 11: 617–629

    MATH  Google Scholar 

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Authors and Affiliations

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

    Remi Tassing, Lei Guo, Jian Liu, HongZhi Lin & GuangXi Zhu

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

    GuangXi Zhu

Authors
  1. Remi Tassing
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  2. Lei Guo
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  3. Jian Liu
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  4. HongZhi Lin
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  5. GuangXi Zhu
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Correspondence to Lei Guo.

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Tassing, R., Guo, L., Liu, J. et al. Gene sorting in differential evolution with cross-generation mutation. Sci. China Inf. Sci. 54, 268–278 (2011). https://doi.org/10.1007/s11432-010-4149-8

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  • DOI: https://doi.org/10.1007/s11432-010-4149-8

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