Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/23712
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dc.contributor.authorLi, Jingpengen_UK
dc.contributor.authorBai, Ruibinen_UK
dc.contributor.authorShen, Yindongen_UK
dc.contributor.authorQu, Rongen_UK
dc.date.accessioned2017-06-09T22:56:00Z-
dc.date.available2017-06-09T22:56:00Z-
dc.date.issued2015-05en_UK
dc.identifier.urihttp://hdl.handle.net/1893/23712-
dc.description.abstractThis paper presents a state transition based formal framework for a new search method, called Evolutionary Ruin and Stochastic Recreate, which tries to learn and adapt to the changing environments during the search process. It improves the performance of the original Ruin and Recreate principle by embedding an additional phase of Evolutionary Ruin to mimic the survival-of-the-fittest mechanism within single solutions. This method executes a cycle of Solution Decomposition, Evolutionary Ruin, Stochastic Recreate and Solution Acceptance until a certain stopping condition is met. The Solution Decomposition phase first uses some problem-specific knowledge to decompose a complete solution into its components and assigns a score to each component. The Evolutionary Ruin phase then employs two evolutionary operators (namely Selection and Mutation) to destroy a certain fraction of the solution, and the next Stochastic Recreate phase repairs the “broken” solution. Last, the Solution Acceptance phase selects a specific strategy to determine the probability of accepting the newly generated solution. Hence, optimisation is achieved by an iterative process of component evaluation, solution disruption and stochastic constructive repair. From the state transitions point of view, this paper presents a probabilistic model and implements a Markov chain analysis on some theoretical properties of the approach. Unlike the theoretical work on genetic algorithm and simulated annealing which are based on state transitions within the space of complete assignments, our model is based on state transitions within the space of partial assignments. The exam timetabling problems are used to test the performance in solving real-world hard problems.en_UK
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.relationLi J, Bai R, Shen Y & Qu R (2015) Search with Evolutionary Ruin and Stochastic Rebuild: a Theoretic Framework and a Case Study on Exam Timetabling. European Journal of Operational Research, 242 (3), pp. 798-806. https://doi.org/10.1016/j.ejor.2014.11.002en_UK
dc.rightsThis item has been embargoed for a period. During the embargo 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.subjectMetaheuristicsen_UK
dc.subjectEvolutionary algorithmen_UK
dc.subjectStochastic processen_UK
dc.subjectCombinatorial optimisationen_UK
dc.subjectExam timetablingen_UK
dc.titleSearch with Evolutionary Ruin and Stochastic Rebuild: a Theoretic Framework and a Case Study on Exam Timetablingen_UK
dc.typeJournal Articleen_UK
dc.rights.embargoreason[li-et-al-EJOR-2015.pdf] Publisher requires embargo of 24 months after formal publication.en_UK
dc.identifier.doi10.1016/j.ejor.2014.11.002en_UK
dc.citation.jtitleEuropean Journal of Operational Researchen_UK
dc.citation.issn0377-2217en_UK
dc.citation.volume242en_UK
dc.citation.issue3en_UK
dc.citation.spage798en_UK
dc.citation.epage806en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusAM - Accepted Manuscripten_UK
dc.contributor.funderEngineering and Physical Sciences Research Councilen_UK
dc.author.emailjli@cs.stir.ac.uken_UK
dc.citation.date13/11/2014en_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationUniversity of Nottingham Ningbo Chinaen_UK
dc.contributor.affiliationHuazhong University of Science and Technologyen_UK
dc.contributor.affiliationUniversity of Nottinghamen_UK
dc.identifier.isiWOS:000348953300009en_UK
dc.identifier.scopusid2-s2.0-84920519768en_UK
dc.identifier.wtid561721en_UK
dc.contributor.orcid0000-0002-6758-0084en_UK
dc.date.accepted2014-11-02en_UK
dcterms.dateAccepted2014-11-02en_UK
dc.date.filedepositdate2016-06-30en_UK
dc.relation.funderprojectDAASE: Dynamic Adaptive Automated Software Engineeringen_UK
dc.relation.funderrefEP/J017515/1en_UK
rioxxterms.apcnot requireden_UK
rioxxterms.typeJournal Article/Reviewen_UK
rioxxterms.versionAMen_UK
local.rioxx.authorLi, Jingpeng|0000-0002-6758-0084en_UK
local.rioxx.authorBai, Ruibin|en_UK
local.rioxx.authorShen, Yindong|en_UK
local.rioxx.authorQu, Rong|en_UK
local.rioxx.projectEP/J017515/1|Engineering and Physical Sciences Research Council|http://dx.doi.org/10.13039/501100000266en_UK
local.rioxx.freetoreaddate2016-11-14en_UK
local.rioxx.licencehttp://www.rioxx.net/licenses/under-embargo-all-rights-reserved||2016-11-13en_UK
local.rioxx.licencehttp://www.rioxx.net/licenses/all-rights-reserved|2016-11-14|en_UK
local.rioxx.filenameli-et-al-EJOR-2015.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source0377-2217en_UK
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