Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/31391
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dc.contributor.authorLi, Jingpengen_UK
dc.contributor.authorAickelin, Uween_UK
dc.date.accessioned2020-07-04T00:05:03Z-
dc.date.available2020-07-04T00:05:03Z-
dc.date.issued2003-12en_UK
dc.identifier.urihttp://hdl.handle.net/1893/31391-
dc.description.abstractA Bayesian optimization algorithm for the nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse's assignment. Unlike our previous work that used GAs to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. eventually, we will be able to identify and mix building blocks directly. The Bayesian optimization algorithm is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed approach might be suitable for other scheduling problems.en_UK
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.relationLi J & Aickelin U (2003) A Bayesian optimization algorithm for the nurse scheduling problem. In: The 2003 Congress on Evolutionary Computation, 2003. CEC '03. The 2003 Congress on Evolutionary Computation, 2003. CEC '03., Canberra, Australia, 08.12.2003-12.12.2003. Piscataway, NJ, USA: IEEE. https://doi.org/10.1109/cec.2003.1299938en_UK
dc.rightsThe publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.en_UK
dc.subjectBayesian methodsen_UK
dc.subjectScheduling algorithmen_UK
dc.subjectProcessor schedulingen_UK
dc.subjectHumansen_UK
dc.subjectComputer networksen_UK
dc.subjectLearning systemsen_UK
dc.subjectGenetic algorithmsen_UK
dc.subjectDecodingen_UK
dc.subjectAlgorithm design and analysisen_UK
dc.subjectDesign optimizationen_UK
dc.titleA Bayesian optimization algorithm for the nurse scheduling problemen_UK
dc.typeConference Paperen_UK
dc.rights.embargodate2999-12-31en_UK
dc.rights.embargoreason[01299938.pdf] The publisher does not allow this work to be made publicly available in this Repository therefore there is an embargo on the full text of the work.en_UK
dc.identifier.doi10.1109/cec.2003.1299938en_UK
dc.citation.publicationstatusPublisheden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.author.emailjli@cs.stir.ac.uken_UK
dc.citation.btitleThe 2003 Congress on Evolutionary Computation, 2003. CEC '03en_UK
dc.citation.conferencedates2003-12-08 - 2003-12-12en_UK
dc.citation.conferencelocationCanberra, Australiaen_UK
dc.citation.conferencenameThe 2003 Congress on Evolutionary Computation, 2003. CEC '03.en_UK
dc.citation.date24/05/2004en_UK
dc.citation.isbn0780378040en_UK
dc.publisher.addressPiscataway, NJ, USAen_UK
dc.contributor.affiliationUniversity of Bradforden_UK
dc.contributor.affiliationUniversity of Bradforden_UK
dc.identifier.scopusid2-s2.0-84901408818en_UK
dc.identifier.wtid1454980en_UK
dc.contributor.orcid0000-0002-6758-0084en_UK
dc.date.filedepositdate2020-06-19en_UK
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings

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