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Generalization and Property Analysis of GENET

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Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3496))

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Abstract

GENET model has attracted much attention for its special feature in solving constraint satisfaction problems. However, the parameter setting problems have not been discussed in detail. In this paper, the convergent behavior of GENET is thoroughly analyzed and its learning strategy is generalized. The obtained results can shed light on choosing parameter values and exploiting problem specific information.

Supported by the Nature Science Foundation of China under Grant 60473034 and the Youth Science Foundation of Shanxi Province under Grant 20031028.

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References

  1. Milis, P., Tsang, E.P.K.: Guided Local Search for Solving SAT andWeighted MAXSAT Problems. Journal of Automated Reasoning 24, 205–223 (2000)

    Article  MathSciNet  Google Scholar 

  2. Tsang, E.P.K., Wang, C.-J., Davenport, A., Voudouris, C., Lau, T.-L.: A Family of Stochastic Methods for Constraint Satisfaction and Optimization. In: The First International Conference on The Practical Application of Constraint Technologies and Logic Programming, London, April 1999, pp. 359–383 (1999)

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  3. Voudouris, C., Tsang, E.P.K.: Guided Local Search and Its Application to the Traveling Salesman Problem. European Journal of Operational Research 113, 469–499 (1999)

    Article  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, Shaoxing College of Arts and Sciences, Shaoxing, 312000, China

    Youmei Li

  2. Institute for Information and System Sciences, Faculty of Science, Xi’an Jiaotong University, Xi’an, Shaan’xi, 710049, China

    Zongben Xu

  3. Department of Information and Mathematics Sciences, College of Science, China Jiliang University, Hangzhou, 310018, China

    Feilong Cao

Authors
  1. Youmei Li
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  2. Zongben Xu
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  3. Feilong Cao
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiaofeng Liao

  3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

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© 2005 Springer-Verlag Berlin Heidelberg

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Li, Y., Xu, Z., Cao, F. (2005). Generalization and Property Analysis of GENET. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_9

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  • DOI: https://doi.org/10.1007/11427391_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25912-1

  • Online ISBN: 978-3-540-32065-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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