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undergraduate thesis
Optimiranje strategija za igru funkcija uz pomoć simboličke regresije
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:864629
Jelić, Mislav
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Jelić, M. (2023). Optimiranje strategija za igru funkcija uz pomoć simboličke regresije (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:864629

Jelić, Mislav. "Optimiranje strategija za igru funkcija uz pomoć simboličke regresije." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:864629

Jelić, Mislav. "Optimiranje strategija za igru funkcija uz pomoć simboličke regresije." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:864629

Jelić, M. (2023). 'Optimiranje strategija za igru funkcija uz pomoć simboličke regresije', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 22 July 2024, https://urn.nsk.hr/urn:nbn:hr:168:864629

Jelić M. Optimiranje strategija za igru funkcija uz pomoć simboličke regresije [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2024 July 22] Available at: https://urn.nsk.hr/urn:nbn:hr:168:864629

M. Jelić, "Optimiranje strategija za igru funkcija uz pomoć simboličke regresije", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:864629

Metadata
TitleOptimiranje strategija za igru funkcija uz pomoć simboličke regresije
Title (english)Optimization of function game strategies with symbolic regression
AuthorMislav Jelić
MentorDomagoj Jakobović (mentor)
Committee memberDomagoj Jakobović (predsjednik povjerenstva)
Committee memberJosip Jukić (član povjerenstva)
Committee memberNIna Drobac (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2023-07-05, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Ovaj rad je opisao što su evolucijski algoritmi, neke vrste evolucijskih algoritama te kako i gdje se koriste, te demonstrirao njihovu uporabu u rješavanju problema aproksimacije funkcija preko igre funkcija. Strategije za aproksimiranje funkcije bile su optimizirane oslanjajući se na simboličku regresiju, a populacije su bile popunjene jedinkama građenim od različitih vrsta modela. Pokazalo se da je genetsko programiranje bio najuspješniji model te samom svojom građom prikladan za aproksimaciju funkcija, jer su potencijalna rješenja koje GP vraća upravo funkcije. Problem odabira ulaznih parametara bio je riješen sa aktivnim učenjem, gdje su se nakon prvih n parametara svi ostali birali principom aktivnog učenja, što je poprilično ubrzalo aproksimiranje funkcija. Pokazano je da kombinacija evolucijskih algoritama sa aktivnim učenjem može značajno poboljšati aproksimiranje funkcija.
Abstract (english)
This paper described what evolutionary algorithms are and explained some of the types of evolutionary algorithms. It is also mentioned how and where they are used, and their use is demonstrated in solving the problem of approximation of functions through the game of functions. Strategies for approximating the function were optimized relying on symbolic regression, and populations were filled with individuals constructed from different types of models. It turned out that genetic programming was the most successful model and, by its very structure, suitable for the approximation of functions, because the potential solutions returned by GP are precisely functions. The problem of selecting input parameters was solved with active learning, where after the first n parameters, all others were selected by the principle of active learning, which considerably accelerated the approximation of functions. It is shown that the combination of evolutionary algorithms with active learning can significantly improve the approximation of functions.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:864629
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-10-11 09:09:38