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dc.contributor.authorLane, P.C.R.
dc.contributor.authorGobet, F.
dc.date.accessioned2011-04-07T11:25:28Z
dc.date.available2011-04-07T11:25:28Z
dc.date.issued2005
dc.identifier.citationLane , P C R & Gobet , F 2005 , ' Discovering predictive variables when evolving cognitive models ' , Lecture Notes in Computer Science , no. 3rd Int Conf on Advances in Pattern Recognition , pp. 108-117 . https://doi.org/10.1007/11551188_12
dc.identifier.issn0302-9743
dc.identifier.otherPURE: 92692
dc.identifier.otherPURE UUID: 5520ecf0-2128-45ef-9e7f-deb37f12f63f
dc.identifier.otherdspace: 2299/5575
dc.identifier.otherScopus: 27244441471
dc.identifier.urihttp://hdl.handle.net/2299/5575
dc.description“The original publication is available at www.springerlink.com”. Copyright Springer
dc.description.abstractA non-dominated sorting genetic algorithm is used to evolve models of learning from di erent theories for multiple tasks. Correlation analysis is performed to identify parameters which affect performance on specific tasks; these are the predictive variables. Mutation is biased so that changes to parameter values tend to preserve values within the population's current range. Experimental results show that optimal models are evolved, and also that uncovering predictive variables is beneficial in improving the rate of convergence.en
dc.language.isoeng
dc.relation.ispartofLecture Notes in Computer Science
dc.titleDiscovering predictive variables when evolving cognitive modelsen
dc.contributor.institutionSchool of Computer Science
dc.description.statusPeer reviewed
rioxxterms.versionofrecordhttps://doi.org/10.1007/11551188_12
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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