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Hybridization of evolutionary algorithms and local search by means of a clustering method

dc.contributor.authorMartínez Estudillo, Alfonso Carlos 
dc.contributor.authorHervas Martínez, César
dc.contributor.authorMartínez Estudillo, Francisco José 
dc.contributor.authorGarcía Pedrajas, N.
dc.date.accessioned2019-02-04T15:19:12Z
dc.date.available2019-02-04T15:19:12Z
dc.date.issued2005
dc.identifier.urihttp://hdl.handle.net/20.500.12412/1011
dc.description.abstractThis paper presents a hybrid evolutionary algorithm (EA) to solve nonlinear-regression problems. Although EAs have proven their ability to explore large search spaces, they are comparatively inefficient in fine tuning the solution. This drawback is usually avoided by means of local optimization algorithms that are applied to the individuals of the population. The algorithms that use local optimization procedures are usually called hybrid algorithms. On the other hand, it is well known that the clustering process enables the creation of groups (clusters) with mutually close points that hopefully correspond to relevant regions of attraction. Local-search procedures can then be started once in every such region.es
dc.language.isoenges
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleHybridization of evolutionary algorithms and local search by means of a clustering methodes
dc.typearticlees
dc.identifier.doi10.1109/TSMCB.2005.860138
dc.issue.number3es
dc.journal.titleIeee Transactions On Systems, Man And Cybernetics, Part B: Cyberneticses
dc.page.initial534es
dc.page.final545es
dc.rights.accessRightsopenAccesses
dc.subject.keywordclusteringes
dc.subject.keywordevolutionary algorithms (EAs)es
dc.subject.keywordhybridizationes
dc.subject.keywordproduct-units networkses
dc.volume.number36es


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional