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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.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.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.subjectÀrees temàtiques de la UPC::Informàtica::Informàtica teòrica
dc.subject.lcshComputational grids (Computer systems)
dc.subject.lcshGenetic algorithms
dc.subject.otherStruggle Strategy
dc.subject.otherSimilarity measure
dc.titleTuning struggle strategy in genetic algorithms for scheduling in computational grids
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.rights.accessOpen Access
dc.description.versionPostprint (author's final draft)
local.citation.authorXhafa, F.; Duran, B.; Abraham, A.; Dahal, K. P.
local.citation.publicationNameNeural network world

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