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Entropic sampling in genetic-entropic algorithm

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Simulated Evolution and Learning (SEAL 1996)

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

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Abstract

A new combinatorial optimization algorithm, genetic-entropic algorithm, is proposed. Based on the entropic sampling, this algorithm shows better performance than the conventional genetic algorithm. We, in particular, test the algorithm using the NK-model. The higher rugged ness of the K value, the better this algorithm performs. The characteristics of the entropic sampling in this algorithm together with the difference between two algorithms are discussed.

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Author information

Authors and Affiliations

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

    Chang-Yong Leeb & Seung Kee Han

  2. Department of Physics, Chungbuk National University, 360-763, Cheongju, Chungbuk, Korea

    Seung Kee Han

Authors
  1. Chang-Yong Leeb
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  2. Seung Kee Han
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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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Leeb, CY., Han, S.K. (1997). Entropic sampling in genetic-entropic algorithm. 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/BFb0028521

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  • DOI: https://doi.org/10.1007/BFb0028521

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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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