Tracking differential evolution algorithms: An adaptive approach through multinomial distribution tracking with exponential forgetting
dc.creator | Epitropakis, M. G. | en |
dc.creator | Tasoulis, D. K. | en |
dc.creator | Pavlidis, N. G. | en |
dc.creator | Plagianakos, V. P. | en |
dc.creator | Vrahatis, M. N. | en |
dc.date.accessioned | 2015-11-23T10:26:19Z | |
dc.date.available | 2015-11-23T10:26:19Z | |
dc.date.issued | 2012 | |
dc.identifier | 10.1007/978-3-642-30448-4_27 | |
dc.identifier.isbn | 9783642304477 | |
dc.identifier.issn | 3029743 | |
dc.identifier.uri | http://hdl.handle.net/11615/27375 | |
dc.description.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. © 2012 Springer-Verlag. | en |
dc.source.uri | http://www.scopus.com/inward/record.url?eid=2-s2.0-84861672367&partnerID=40&md5=ff5f6030bc879d934f2fc224a44a5cf3 | |
dc.subject | Adaptation | en |
dc.subject | Differential Evolution | en |
dc.subject | Exponential forgetting | en |
dc.subject | Multinomial Distribution | en |
dc.subject | Adaptation framework | en |
dc.subject | Adaptive approach | en |
dc.subject | Adaptive filter theory | en |
dc.subject | Benchmark functions | en |
dc.subject | Differential evolution algorithms | en |
dc.subject | Evolutionary process | en |
dc.subject | Multinomial distributions | en |
dc.subject | Search dynamics | en |
dc.subject | State-of-the-art algorithms | en |
dc.subject | Adaptive filters | en |
dc.subject | Artificial intelligence | en |
dc.subject | Dynamics | en |
dc.subject | Evolutionary algorithms | en |
dc.title | Tracking differential evolution algorithms: An adaptive approach through multinomial distribution tracking with exponential forgetting | en |
dc.type | other | en |
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