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Tracking differential evolution algorithms: an adaptive approach through multinomial distribution tracking with exponential forgetting

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter

Published
  • Michael Epitropakis
  • Dimitrios Tasoulis
  • Nicos Pavlidis
  • Vassilis P. Plagianakos
  • Michael N. Vrahatis
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Publication date2012
Host publicationArtificial Intelligence: Theories and Applications: 7th Hellenic Conference on AI, SETN 2012, Lamia, Greece, May 28-31, 2012. Proceedings
EditorsIlias Maglogianis, Vassilis Plagianakos, Ioannis Vlahavas
Place of PublicationBerlin
PublisherSpringer Verlag
Pages214-222
Number of pages9
ISBN (Electronic)978-3-642-30448-4
ISBN (Print)978-3-642-30447-7
Original languageEnglish

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume7297
ISSN (Print)0302-9743

Abstract

Several Differential Evolution variants with modified search dynamics have been recently proposed, to improve the performance of the method. This work borrows ideas from adaptive filter theory to develop an “online” algorithmic adaptation framework. The proposed framework is based on tracking the parameters of a multinomial distribution to reflect changes in the evolutionary process. As such, we design a multinomial distribution tracker to capture the successful evolution movements of three Differential Evolution algorithms, in an attempt to aggregate their characteristics and their search dynamics. Experimental results on ten benchmark functions and comparisons with five state-of-the-art algorithms indicate that the proposed framework is competitive and very promising.