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Tightness Time for the Linkage Learning Genetic Algorithm

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Genetic and Evolutionary Computation — GECCO 2003 (GECCO 2003)

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Abstract

This paper develops a model for tightness time, linkage learning time for a single building block, in the linkage learning genetic algorithm (LLGA). First, the existing models for both linkage learning mechanisms, linkage skew and linkage shift, are extended and investigated. Then, the tightness time model is derived and proposed based on the extended linkage learning mechanism models. Experimental results are also presented in this study to verify the extended models for linkage learning mechanisms and the proposed model for tightness time.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Department of General Engineering, University of Illinois, Urbana, IL, 61801, USA

    Ying-ping Chen

  2. Department of General Engineering, University of Illinois, Urbana, IL, 61801, USA

    David E. Goldberg

Authors
  1. Ying-ping Chen
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  2. David E. Goldberg
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Editor information

Editors and Affiliations

  1. Lawrence Livermore National Laboratory, Center for Applied Scientific Computing (CASC), 7000 East Avenue, L-561, Livermore, CA, 94550, USA

    Erick Cantú-Paz

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© 2003 Springer-Verlag Berlin Heidelberg

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Chen, Yp., Goldberg, D.E. (2003). Tightness Time for the Linkage Learning Genetic Algorithm. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45105-6_97

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  • DOI: https://doi.org/10.1007/3-540-45105-6_97

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40602-0

  • Online ISBN: 978-3-540-45105-1

  • eBook Packages: Springer Book Archive

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