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Intelligent Systems Design and Applications pp 425–436Cite as

Population Size Adaptation for Differential Evolution Algorithm Using Fuzzy Logic

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Part of the book series: Advances in Soft Computing ((AINSC,volume 23))

Abstract

The Differential Evolution algorithm is a floating-point encoded Evolutionary Algorithm for global optimization over continuous spaces. The objective of this study is to introduce a dynamically controlled adaptive population size for the Differential Evolution algorithm by the means of a fuzzy controller. The controller’s inputs incorporate the changes in objective function values and individual solution vectors between the populations of two successive generations. The fuzzy controller then uses these data for dynamically adapting the population size. The obtained preliminary results suggest that the adaptive population size may result in a higher convergence rate and reduce the number of objective function evaluations required.

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Author information

Authors and Affiliations

  1. Department of Information Technology, Lappeenranta University of Technology, P.O.Box 20, FIN-53851, Lappeenranta, Finland

    Junhong Liu & Jouni Lampinen

Authors
  1. Junhong Liu
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  2. Jouni Lampinen
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Editor information

Editors and Affiliations

  1. Oklahoma State University, Tulsa, OK, USA

    Ajith Abraham

  2. Fraunhofer Institute for Production Systems and Design Technology, Berlin, Germany

    Katrin Franke  & Mario Köppen  & 

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© 2003 Springer-Verlag Berlin Heidelberg

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Liu, J., Lampinen, J. (2003). Population Size Adaptation for Differential Evolution Algorithm Using Fuzzy Logic. In: Abraham, A., Franke, K., Köppen, M. (eds) Intelligent Systems Design and Applications. Advances in Soft Computing, vol 23. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44999-7_41

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  • DOI: https://doi.org/10.1007/978-3-540-44999-7_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40426-2

  • Online ISBN: 978-3-540-44999-7

  • eBook Packages: Springer Book Archive

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