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

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Tracking differential evolution algorithms: an adaptive approach through multinomial distribution tracking with exponential forgetting. / Epitropakis, Michael; Tasoulis, Dimitrios; Pavlidis, Nicos et al.
Artificial Intelligence: Theories and Applications: 7th Hellenic Conference on AI, SETN 2012, Lamia, Greece, May 28-31, 2012. Proceedings. ed. / Ilias Maglogianis; Vassilis Plagianakos; Ioannis Vlahavas. Berlin: Springer Verlag, 2012. p. 214-222 (Lecture Notes in Computer Science; Vol. 7297).

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

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

Epitropakis, Michael ; Tasoulis, Dimitrios ; Pavlidis, Nicos et al. / Tracking differential evolution algorithms : an adaptive approach through multinomial distribution tracking with exponential forgetting. Artificial Intelligence: Theories and Applications: 7th Hellenic Conference on AI, SETN 2012, Lamia, Greece, May 28-31, 2012. Proceedings. editor / Ilias Maglogianis ; Vassilis Plagianakos ; Ioannis Vlahavas. Berlin : Springer Verlag, 2012. pp. 214-222 (Lecture Notes in Computer Science).

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 -