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dc.contributor.authorLane, Peter
dc.contributor.authorGobet, Fernand
dc.date.accessioned2014-06-04T09:00:30Z
dc.date.available2014-06-04T09:00:30Z
dc.date.issued2014-04-25
dc.identifier.citationLane , P & Gobet , F 2014 , ' Evolving Non-Dominated Parameter Sets for Computational Models from Multiple Experiments ' , Journal of Artificial General Intelligence , vol. 4 , no. 1 . https://doi.org/10.2478/jagi-2013-0001
dc.identifier.issn1946-0163
dc.identifier.otherPURE: 2117213
dc.identifier.otherPURE UUID: 9abfcd17-fd77-4edc-a213-d0a107b08465
dc.identifier.urihttp://hdl.handle.net/2299/13607
dc.description© Peter C. R. Lane, Fernand Gobet. This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY-NC 3.0)
dc.description.abstractCreating robust, reproducible and optimal computational models is a key challenge for theorists in many sciences. Psychology and cognitive science face particular challenges as large amounts of data are collected and many models are not amenable to analytical techniques for calculating parameter sets. Particular problems are to locate the full range of acceptable model parameters for a given dataset, and to confirm the consistency of model parameters across different datasets. Resolving these problems will provide a better understanding of the behaviour of computational models, and so support the development of general and robust models. In this article, we address these problems using evolutionary algorithms to develop parameters for computational models against multiple sets of experimental data; in particular, we propose the ‘speciated non-dominated sorting genetic algorithm’ for evolving models in several theories. We discuss the problem of developing a model of categorisation using twenty-nine sets of data and models drawn from four different theories. We find that the evolutionary algorithms generate high quality models, adapted to provide a good fit to all available data.en
dc.format.extent30
dc.language.isoeng
dc.relation.ispartofJournal of Artificial General Intelligence
dc.subjectcognitive modelling
dc.subjectoptimisation
dc.subjectmodel comparison
dc.subjectevolutionary algorithms
dc.titleEvolving Non-Dominated Parameter Sets for Computational Models from Multiple Experimentsen
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.description.statusPeer reviewed
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.2478/jagi-2013-0001
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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