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master's thesis
Rješavanje problema raspoređivanja korištenjem suparničkog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:067691
Duspara, Ivica
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Duspara, I. (2021). Rješavanje problema raspoređivanja korištenjem suparničkog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:067691

Duspara, Ivica. "Rješavanje problema raspoređivanja korištenjem suparničkog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:067691

Duspara, Ivica. "Rješavanje problema raspoređivanja korištenjem suparničkog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:067691

Duspara, I. (2021). 'Rješavanje problema raspoređivanja korištenjem suparničkog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 17 May 2024, https://urn.nsk.hr/urn:nbn:hr:168:067691

Duspara I. Rješavanje problema raspoređivanja korištenjem suparničkog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2024 May 17] Available at: https://urn.nsk.hr/urn:nbn:hr:168:067691

I. Duspara, "Rješavanje problema raspoređivanja korištenjem suparničkog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:067691

Metadata
TitleRješavanje problema raspoređivanja korištenjem suparničkog učenja
Title (english)Solving scheduling problems with adversarial learning
AuthorIvica Duspara
MentorDomagoj Jakobović (mentor)
Committee memberDomagoj Jakobović (predsjednik povjerenstva)
Committee memberMarko Đurasević (član povjerenstva)
Committee memberMarko Čupić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2021-07-14, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Većina problema raspoređivanja su NP teški problemi. Korištenjem strojnog učenja moguće je pronaći u prihvatljivom vremenu neko, ne nužno najbolje, rješenje nekog problema raspoređivanja. U suparničkom učenju osim raspoređivača poslova, uče i sami poslovi kako bi otežali proces raspoređivanja raspoređivačima. Koristeći genetsko programiranje, populacija raspoređivača i problema se međusobno natječe - koevoluiraju. Implementacija je ostvarena u radnom okviru ECF. Dani rezultati su prihvatljivi.
Abstract (english)
Most of scheduling problems are NP hard problem. Using machine learning it is possible to find an acceptable solution (not necessary the best solution) to some scheduling problem. In adversarial instead of just schedulers, both schedulers and jobs learn. Jobs learn to make process of scheduling more difficult. Using genetic programming, population of schedulers and population of jobs are competing - coevolving. Implementation is done in ECF framework. Results of coevolution are acceptable.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:067691
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsClosed access
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Created on2023-01-13 12:10:10