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Description
  • A new adaptive Differential Evolution algorithm called EWMA-DECr is proposed. In original Differential Evolution algorithm three different control parameter values must be pre-specified by the user a priori: population size, crossover and mutation scale factor. Choosing good parameters can be very difficult for the user, especially for the practitioners. In the proposed algorithm the crossover factor is adapted using a novel exponential moving average based mechanism, while the other control parameters are kept fixed as in standard Differential Evolution. The algorithm was initially evaluated by using the set of 25 benchmark functions provided by CEC2005 special session on real-parameter optimization and compared with the results of standard DE/rand/1/bin version. EWMA-DECr outperformed the original Differential Evolution in half of tested cases, which is demonstrating the potential of the proposed adaptation approach.
  • A new adaptive Differential Evolution algorithm called EWMA-DECr is proposed. In original Differential Evolution algorithm three different control parameter values must be pre-specified by the user a priori: population size, crossover and mutation scale factor. Choosing good parameters can be very difficult for the user, especially for the practitioners. In the proposed algorithm the crossover factor is adapted using a novel exponential moving average based mechanism, while the other control parameters are kept fixed as in standard Differential Evolution. The algorithm was initially evaluated by using the set of 25 benchmark functions provided by CEC2005 special session on real-parameter optimization and compared with the results of standard DE/rand/1/bin version. EWMA-DECr outperformed the original Differential Evolution in half of tested cases, which is demonstrating the potential of the proposed adaptation approach. (en)
Title
  • A Crossover Adaptation Mechanism for Differential Evolution Algorithm
  • A Crossover Adaptation Mechanism for Differential Evolution Algorithm (en)
skos:prefLabel
  • A Crossover Adaptation Mechanism for Differential Evolution Algorithm
  • A Crossover Adaptation Mechanism for Differential Evolution Algorithm (en)
skos:notation
  • RIV/61989100:27740/14:86092565!RIV15-MSM-27740___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(EE.2.3.20.0072)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
  • Lampinen, Jouni
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 677
http://linked.open...ai/riv/idVysledku
  • RIV/61989100:27740/14:86092565
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Exponential moving average; Differential evolution; Control parameter; Adaptation (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [12B8FD28329D]
http://linked.open...v/mistoKonaniAkce
  • Brno
http://linked.open...i/riv/mistoVydani
  • Brno
http://linked.open...i/riv/nazevZdroje
  • MENDEL 2014 : 20th International Conference on Soft Computing : June 25-27, 2014, Brno, Czech Republic
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Lampinen, Jouni
  • Aalto, J.
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
issn
  • 1803-3814
number of pages
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  • Vysoké učení technické v Brně
https://schema.org/isbn
  • 978-80-214-4984-8
http://localhost/t...ganizacniJednotka
  • 27740
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