Differential Evolution Algorithms with Cellular Populations

Reference : Differential Evolution Algorithms with Cellular Populations


	Document type :	Scientific journals : Article
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/16695

Title :	Differential Evolution Algorithms with Cellular Populations
Language :	English
Author, co-author :	Dorronsoro, Bernabé [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Bouvry, Pascal [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	2011
Journal title :	Lecture Notes in Computer Science
Publisher :	Springer
Volume :	6239
Pages :	320-330
Peer reviewed :	Yes
Audience :	International
ISSN :	0302-9743
e-ISSN :	1611-3349
Keywords :	[en] differntial evolution ; cellular populations
Abstract :	[en] Differential Evolution (DE) algorithms are efficient Evolutionary Algorithms (EAs) for the continuous optimization domain. There exist a large number of DE variants in the literature. In this paper, we analyze the effect of adding a cellular structure to the population of some of the most outstanding existing ones. The original algorithms will be compared versus their equivalent versions with cellular population both in terms of accuracy and convergence speed. As a result, we conclude that the cellular versions of the algorithms perform, in general, better than the equivalent state-of-the-art ones in the two considered issues.
Research centres :	University of Luxembourg: High Performance Computing - ULHPC
Permalink :	http://hdl.handle.net/10993/16695
DOI :	10.1007/978-3-642-15871-1_33

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