Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/775
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dc.contributor.authorLane, PCR-
dc.contributor.authorGobet, F-
dc.coverage.spatial10en
dc.date.accessioned2007-05-18T11:38:15Z-
dc.date.available2007-05-18T11:38:15Z-
dc.date.issued2005-
dc.identifier.citationThird International Conference on Advances in Pattern Recognitionen
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/775-
dc.description.abstractA non-dominated sorting genetic algorithm is used to evolve models of learning from different 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.format.extent131191 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen-
dc.publisherThird International Conference on Advances in Pattern Recognition.en
dc.subjectGenetic algorithmen
dc.subjectNon-dominateden
dc.subjectModelen
dc.subjectTheory developmenten
dc.subjectComputational modellingen
dc.subjectConcept formationen
dc.subjectCHRESTen
dc.subjectConnectionismen
dc.titleDiscovering predictive variables when evolving cognitive modelsen
dc.typeResearch Paperen
Appears in Collections:Psychology
Dept of Life Sciences Research Papers

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