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Migration Model of Adaptive Differential Evolution Applied to Real-World Problems

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Artificial Intelligence and Soft Computing (ICAISC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10841))

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Abstract

Ten variants of migration model are compared with six adaptive differential evolution (DE) algorithms on real-world problems. Two parameters of migration model are studied experimentally. The results of experiments demonstrate the superiority of the migration models in first stages of the search process. A success of adaptive DE algorithms employed by migration model is systematically influenced by the studied parameters. The most efficient algorithm in the comparison is proposed migration model P15x50. The worst performing algorithm is adaptive DE.

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Author information

Authors and Affiliations

  1. Department of Informatics and Computers, University of Ostrava, 30. dubna 22, 70103, Ostrava, Czech Republic

    Petr Bujok

Authors
  1. Petr Bujok
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Correspondence to Petr Bujok .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Bujok, P. (2018). Migration Model of Adaptive Differential Evolution Applied to Real-World Problems. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10841. Springer, Cham. https://doi.org/10.1007/978-3-319-91253-0_30

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  • DOI: https://doi.org/10.1007/978-3-319-91253-0_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91252-3

  • Online ISBN: 978-3-319-91253-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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