Dynamic Configuration of Differential Evolution Control Parameters and Operators

  • Saber Mohammed ElsayedEmail author
  • Ruhul Sarker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9592)


Differential evolution has shown success in solving different optimization problems. However, its performance depends on the control parameters and search operators. Different from existing approaches, in this paper, a new framework which dynamically configures the appropriate choices of operators and parameters is introduced, in which the success of a search operator is linked to the proper combination of control parameters (scaling factor and crossover rate). Also, an adaptation of the population size is adopted. The performance of the proposed algorithm is assessed using a well-known set of constrained problems with the experimental results demonstrating that it is superior to state-of-the-art algorithms.



This work was supported by an Australian Research Council Discovery Project (Grant# DP150102583) awarded to A/Prof. Ruhul Sarker.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Engineering and Information TechnologyUniversity of New South Wales at CanberraCanberraAustralia

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