Show simple item record

dc.contributor.authorAlexeeva, Tatyana
dc.contributor.authorChechurin, Leonid
dc.contributor.authorDodonov, Viktor
dc.contributor.authorHonarmand, Zahra
dc.contributor.authorKuznetsov, Nikolay
dc.contributor.authorNeittaanmäki, Pekka
dc.date.accessioned2024-02-21T09:48:04Z
dc.date.available2024-02-21T09:48:04Z
dc.date.issued2023
dc.identifier.citationAlexeeva, T., Chechurin, L., Dodonov, V., Honarmand, Z., Kuznetsov, N., & Neittaanmäki, P. (2023). Optimal control and genetic algorithms in modeling dynamical allocation of resources for a three-sector economy. <i>International Journal of Parallel, Emergent and Distributed Systems</i>, <i>38</i>(2), 99-109. <a href="https://doi.org/10.1080/17445760.2022.2136372" target="_blank">https://doi.org/10.1080/17445760.2022.2136372</a>
dc.identifier.otherCONVID_159527976
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/93531
dc.description.abstractThe task of looking for the optimal allocation of resources in an economy is fraught with a number of severe restrictions. This is manifested in the complexity of the technical implementation of the solution even in the case of a low dimension of the problem. In this paper, we consider two approaches, analytical and numerical, for deriving the dynamical optimal allocation of resources in a three-sector economy and show that the use of modern artificial intelligence (AI) technologies such as genetic algorithms (GA), can be useful for expanding the range of effective tools and new contributions to this problem.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherTaylor & Francis
dc.relation.ispartofseriesInternational Journal of Parallel, Emergent and Distributed Systems
dc.rightsIn Copyright
dc.subject.otheroptimal control
dc.subject.othernonlinear dynamics
dc.subject.othereconomic growth
dc.subject.otherbalanced economy
dc.subject.othergenetic algorithms
dc.titleOptimal control and genetic algorithms in modeling dynamical allocation of resources for a three-sector economy
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202402211998
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineComputing, Information Technology and Mathematicsfi
dc.contributor.oppiaineLaskennallinen tiedefi
dc.contributor.oppiaineMathematical Information Technologyen
dc.contributor.oppiaineComputing, Information Technology and Mathematicsen
dc.contributor.oppiaineComputational Scienceen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange99-109
dc.relation.issn1744-5760
dc.relation.numberinseries2
dc.relation.volume38
dc.type.versionacceptedVersion
dc.rights.copyright© 2023 Taylor & Francis
dc.rights.accesslevelopenAccessfi
dc.subject.ysotalouskasvu
dc.subject.ysooptimointi
dc.subject.ysogeneettiset algoritmit
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p6150
jyx.subject.urihttp://www.yso.fi/onto/yso/p13477
jyx.subject.urihttp://www.yso.fi/onto/yso/p7987
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1080/17445760.2022.2136372
dc.type.okmA1


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

In Copyright
Except where otherwise noted, this item's license is described as In Copyright