Show simple item record

dc.contributor.authorXhafa Xhafa, Fatos
dc.contributor.authorDuran, Bernat
dc.contributor.authorAbraham, Ajith
dc.contributor.authorDahal, Keshav P.
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2018-05-03T07:13:10Z
dc.date.available2018-05-03T07:13:10Z
dc.date.issued2008-10
dc.identifier.citationXhafa, F., Duran, B., Abraham, A., Dahal, K. P. Tuning struggle strategy in genetic algorithms for scheduling in computational grids. "Neural network world", Octubre 2008, vol. 18, núm. 3, p. 209-225.
dc.identifier.issn1210-0552
dc.identifier.urihttp://hdl.handle.net/2117/116889
dc.description.abstractJob Scheduling on Computational Grids is gaining importance due to the need for efficient large-scale Grid-enabled applications. Among different optimization techniques addressed for the problem, Genetic Algorithm (GA) is a popular class of solution methods. As GAs are high level algorithms, specific algorithms can be designed by choosing the genetic operators as well as the evolutionary strategies. In this paper we focus on Struggle GAs and their tuning for the scheduling of independent jobs in computational grids. Our results showed that a careful hash implementation for computing the similarity of solutions was able to alleviate the computational burden of Struggle GA and perform better than standard similarity measures.
dc.format.extent17 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Informàtica::Informàtica teòrica
dc.subject.lcshComputational grids (Computer systems)
dc.subject.lcshGenetic algorithms
dc.subject.otherScheduling
dc.subject.otherStruggle Strategy
dc.subject.otherSimilarity measure
dc.subject.otherTuning
dc.titleTuning struggle strategy in genetic algorithms for scheduling in computational grids
dc.typeArticle
dc.subject.lemacComputació distribuïda
dc.subject.lemacAlgorismes genètics
dc.contributor.groupUniversitat Politècnica de Catalunya. ALBCOM - Algorismia, Bioinformàtica, Complexitat i Mètodes Formals
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://www.nnw.cz/obsahy08.html
dc.rights.accessOpen Access
local.identifier.drac805951
dc.description.versionPostprint (author's final draft)
local.citation.authorXhafa, F.; Duran, B.; Abraham, A.; Dahal, K. P.
local.citation.publicationNameNeural network world
local.citation.volume18
local.citation.number3
local.citation.startingPage209
local.citation.endingPage225


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder