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Asia-Pacific Conference on Simulated Evolution and Learning

SEAL 1996: Simulated Evolution and Learning pp 196–205Cite as

Random search based on genetic operators

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

Abstract

This paper presents a new way of optimization based on genetic operators. It has been known that the canonical genetic algorithm (CGA) does not guarantee convergence to the global optimum and also has the problem of premature saturation (or convergence). In this sense, this paper suggests a new way of searching based on genetic operators in which convergence to the global optimum is guaranteed and efficiency in searching is considered. The basic scheme of the suggested approach is to apply the genetic operators in the case of searching for solutions and the random perturbation in the case of premature saturation. To show the effectiveness of our approach, simulation for vector quantization has been performed.

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Author information

Authors and Affiliations

  1. Division of Basic Sciences, Korea Advanced Institute of Science and Technology, 373-1 Kusong-dong, 305-701, Yusong-gu, Taejon, Korea

    Rhee M. Kil

  2. Research Department, Electronics and Telecommunications Research Institute, Yusong P.O. Box 106, 305-600, Taejon, Korea

    Yoonseon Song

Authors
  1. Rhee M. Kil
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  2. Yoonseon Song
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Editor information

Xin Yao Jong-Hwan Kim Takeshi Furuhashi

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© 1997 Springer-Verlag Berlin Heidelberg

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Kil, R.M., Song, Y. (1997). Random search based on genetic operators. 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/BFb0028536

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  • DOI: https://doi.org/10.1007/BFb0028536

  • Published:

  • 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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