Standard
Harvard
Epitropakis, M, Tasoulis, D
, Pavlidis, N, Plagianakos, VP & Vrahatis, MN 2012,
Tracking differential evolution algorithms: an adaptive approach through multinomial distribution tracking with exponential forgetting. in I Maglogianis, V Plagianakos & I Vlahavas (eds),
Artificial Intelligence: Theories and Applications: 7th Hellenic Conference on AI, SETN 2012, Lamia, Greece, May 28-31, 2012. Proceedings. Lecture Notes in Computer Science, vol. 7297, Springer Verlag, Berlin, pp. 214-222.
https://doi.org/10.1007/978-3-642-30448-4_27
APA
Epitropakis, M., Tasoulis, D.
, Pavlidis, N., Plagianakos, V. P., & Vrahatis, M. N. (2012).
Tracking differential evolution algorithms: an adaptive approach through multinomial distribution tracking with exponential forgetting. In I. Maglogianis, V. Plagianakos, & I. Vlahavas (Eds.),
Artificial Intelligence: Theories and Applications: 7th Hellenic Conference on AI, SETN 2012, Lamia, Greece, May 28-31, 2012. Proceedings (pp. 214-222). (Lecture Notes in Computer Science; Vol. 7297). Springer Verlag.
https://doi.org/10.1007/978-3-642-30448-4_27
Vancouver
Epitropakis M, Tasoulis D
, Pavlidis N, Plagianakos VP, Vrahatis MN.
Tracking differential evolution algorithms: an adaptive approach through multinomial distribution tracking with exponential forgetting. In Maglogianis I, Plagianakos V, Vlahavas I, editors, Artificial Intelligence: Theories and Applications: 7th Hellenic Conference on AI, SETN 2012, Lamia, Greece, May 28-31, 2012. Proceedings. Berlin: Springer Verlag. 2012. p. 214-222. (Lecture Notes in Computer Science). doi: 10.1007/978-3-642-30448-4_27
Author
Bibtex
@inbook{7d66e50e808241f78433edbedd75ace4,
title = "Tracking differential evolution algorithms: an adaptive approach through multinomial distribution tracking with exponential forgetting",
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.",
author = "Michael Epitropakis and Dimitrios Tasoulis and Nicos Pavlidis and Plagianakos, {Vassilis P.} and Vrahatis, {Michael N.}",
year = "2012",
doi = "10.1007/978-3-642-30448-4_27",
language = "English",
isbn = "978-3-642-30447-7",
series = "Lecture Notes in Computer Science",
publisher = "Springer Verlag",
pages = "214--222",
editor = "Ilias Maglogianis and Vassilis Plagianakos and Ioannis Vlahavas",
booktitle = "Artificial Intelligence: Theories and Applications",
}
RIS
TY - CHAP
T1 - Tracking differential evolution algorithms
T2 - an adaptive approach through multinomial distribution tracking with exponential forgetting
AU - Epitropakis, Michael
AU - Tasoulis, Dimitrios
AU - Pavlidis, Nicos
AU - Plagianakos, Vassilis P.
AU - Vrahatis, Michael N.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-642-30448-4_27
DO - 10.1007/978-3-642-30448-4_27
M3 - Chapter
SN - 978-3-642-30447-7
T3 - Lecture Notes in Computer Science
SP - 214
EP - 222
BT - Artificial Intelligence: Theories and Applications
A2 - Maglogianis, Ilias
A2 - Plagianakos, Vassilis
A2 - Vlahavas, Ioannis
PB - Springer Verlag
CY - Berlin
ER -