Reference : Study of Different Small-world Topology Generation Mechanisms for Genetic Algorithms

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/16691

Title :	Study of Different Small-world Topology Generation Mechanisms for Genetic Algorithms
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 :	2012
Main document title :	IEEE Congress on Evolutionary Computation
Publisher :	IEEE
Pages :	1580 - 1587
Peer reviewed :	Yes
On invitation :	No
Audience :	International
ISBN :	978-1-4673-1508-1
Event name :	Congress on Evolutionary Computation
Event date :	from 10-06-2012 to 15-06-2012
Event place (city) :	Brisbane
Event country :	Australia
Abstract :	[en] The use of small-world graphs as a topology structure for the population of Evolutionary Algorithms (EAs) has been recently proposed in the literature. The motivation is clear: the high clustering coefficient and low characteristic path length of such networks makes them suitable for fast local information dissemination, while at the same time preventing it from quickly spreading on the whole population, as it happens in panmictic populations. However, even though several papers addressed this issue so far, only a few of them are able to provide competitive results with other panmictic and/or decentralized population EAs with similar configurations. Therefore, we perform ax study in this work, both theoretically and empirically, on the most appropriate mechanisms to generate SW topologies for Genetic Algorithms (a family of EA). The algorithms are analyzed in terms of efficiency and efficacy, and the best studied variant is validated versus other GAs using well known centralized and decentralized population structures, outperforming them.
Research centres :	University of Luxembourg: High Performance Computing - ULHPC
Permalink :	http://hdl.handle.net/10993/16691
Commentary :	Proceedings of the IEEE Congress on Evolutionary Computation (CEC), part of World Conference in Computational Intelligence (WCCI)

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