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An adaptive evolutionary algorithm for numerical optimization

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Book cover Simulated Evolution and Learning (SEAL 1996)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1285))

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Abstract

In this paper, a normalized floating point representation has been used for making it be possible to design biotechnical genetic operators as well as to apply some genetic operators like inversion. To improve the adaptation of evolutionary algorithms and avoid the biases which may exist in some genetic operators, we have designed and applied several kinds of genetic operators with some probability. The experimental results show that our adaptive evolutionary algorithm has a better performance than the BGA (Breeder genetic Algorithm) and GAFOC (Genetic Algorithm For Optimal Control problems) for the test problems.

This work was supported in part by the National Natural Science Foundation of China, the National 863 High Technology Project of China, the Doctoral Program Foundation of State Educational Commission of China and the Natural Science Foundation of Hubei Province of China.

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References

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Authors and Affiliations

  1. State Key Laboratory of Software Engineering Wuhan University, 430072, Wuhan, P.R.China

    Zhengjun Pan & Lishan Kang

Authors
  1. Zhengjun Pan
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  2. Lishan Kang
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Xin Yao Jong-Hwan Kim Takeshi Furuhashi

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© 1997 Springer-Verlag Berlin Heidelberg

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Pan, Z., Kang, L. (1997). An adaptive evolutionary algorithm for numerical optimization. In: Yao, X., Kim, JH., Furuhashi, T. (eds) Simulated Evolution and Learning. SEAL 1996. Lecture Notes in Computer Science, vol 1285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0028518

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  • DOI: https://doi.org/10.1007/BFb0028518

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63399-0

  • Online ISBN: 978-3-540-69538-7

  • eBook Packages: Springer Book Archive

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