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International Conference on Neural Information Processing

ICONIP 2006: Neural Information Processing pp 836–844Cite as

An Improved Primal-Dual Genetic Algorithm for Optimization in Dynamic Environments

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4234))

Abstract

Inspired by the complementary and dominance mechanism in nature, the Primal-Dual Genetic Algorithm (PDGA) has been proved successful in dynamic environments. In this paper, an important operator in PDGA, primal-dual mapping, is discussed and a new statistics-based primal-dual mapping scheme is proposed. The experimental results on the dynamic optimization problems generated from a set of stationary benchmark problems show that the improved PDGA has stronger adaptability and robustness than the original for dynamic optimization problems.

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Author information

Authors and Affiliations

  1. Institute of Systems Engineering, Northeastern University, 110004, P.R. China

    Hongfeng Wang & Dingwei Wang

Authors
  1. Hongfeng Wang
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  2. Dingwei Wang
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Engineering, The Chinese Univ. of Hong Kong, Shatin, N.T., Hong Kong

    Irwin King

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

    Jun Wang

  3. The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Lai-Wan Chan

  4. Department of Computer Science and Engineering & Center for Cognitive Science, The Ohio State University, OH 43210, Columbus

    DeLiang Wang

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© 2006 Springer-Verlag Berlin Heidelberg

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Wang, H., Wang, D. (2006). An Improved Primal-Dual Genetic Algorithm for Optimization in Dynamic Environments. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_92

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  • DOI: https://doi.org/10.1007/11893295_92

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46484-6

  • Online ISBN: 978-3-540-46485-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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