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dc.contributor.authorJin, Yaochu
dc.contributor.authorWang, Handing
dc.contributor.authorChugh, Tinkle
dc.contributor.authorGuo, Dan
dc.contributor.authorMiettinen, Kaisa
dc.date.accessioned2018-09-13T08:59:37Z
dc.date.available2018-09-13T08:59:37Z
dc.date.issued2019fi
dc.identifier.citationJin, Y., Wang, H., Chugh, T., Guo, D., & Miettinen, K. (2019). Data-Driven Evolutionary Optimization : An Overview and Case Studies. <em>IEEE Transactions on Evolutionary Computation</em>. 23 (3), 442-458.  <a href="https://doi.org/10.1109/TEVC.2018.2869001">doi:10.1109/TEVC.2018.2869001</a>fi
dc.identifier.otherTUTKAID_78702
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/59511
dc.description.abstractMost evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward. In solving many real-world optimization problems, however, such objective functions may not exist, instead computationally expensive numerical simulations or costly physical experiments must be performed for fitness evaluations. In more extreme cases, only historical data are available for performing optimization and no new data can be generated during optimization. Solving evolutionary optimization problems driven by data collected in simulations, physical experiments, production processes, or daily life are termed data-driven evolutionary optimization. In this paper, we provide a taxonomy of different data driven evolutionary optimization problems, discuss main challenges in data-driven evolutionary optimization with respect to the nature and amount of data, and the availability of new data during optimization. Real-world application examples are given to illustrate different model management strategies for different categories of data-driven optimization problems.fi
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers
dc.relation.ispartofseriesIEEE Transactions on Evolutionary Computation
dc.rightsIn Copyright
dc.subject.otheroptimointifi
dc.subject.otheralgoritmitfi
dc.subject.othermallintaminenfi
dc.subject.otherkoneoppiminenfi
dc.subject.otherdata-driven optimizationfi
dc.subject.otherevolutionary algorithmsfi
dc.subject.othersurrogatefi
dc.subject.othermodel managementfi
dc.subject.otherdata sciencefi
dc.subject.othermachine learningfi
dc.titleData-Driven Evolutionary Optimization : An Overview and Case Studiesfi
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201809054027
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikka
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2018-09-05T12:15:13Z
dc.description.reviewstatuspeerReviewed
dc.format.pagerange?
dc.relation.issn1089-778X
dc.relation.numberinseries3
dc.relation.volume23
dc.type.versionacceptedVersion
dc.rights.copyright© 2018 IEEE
dc.rights.accesslevelopenAccessfi
dc.format.contentfulltext
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1109/TEVC.2018.2869001


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