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An Evaluative Study of Adaptive Control of Population Size in Differential Evolution

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Artificial Intelligence and Soft Computing (ICAISC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11508))

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Abstract

In this paper, a newly proposed setting of a diversity-based adaptive mechanism of population size setting in differential evolution (DE) is experimentally studied. Seven state-of-the-art adaptive DE variants and classic DE are used in the experiments where 22 real-world problems are solved. The obtained results are assessed by statistical tests. The diversity-based approach often performs substantially better compared with the original fixed population size setting or linearly decreasing population size. A newly proposed setting of the control parameter performs at least the same or better than the original setting.

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Author information

Authors and Affiliations

  1. 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. (2019). An Evaluative Study of Adaptive Control of Population Size in Differential Evolution. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11508. Springer, Cham. https://doi.org/10.1007/978-3-030-20912-4_39

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  • DOI: https://doi.org/10.1007/978-3-030-20912-4_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20911-7

  • Online ISBN: 978-3-030-20912-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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