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Title:
Merjenje podobnosti metahevrističnih iskalnih strategij z modeli strojnega učenja : magistrsko delo
Authors:
ID
Hozjan, Žan
(
Author
)
ID
Strnad, Damjan
(
Mentor
)
More about this mentor...
ID
Fister, Iztok
(
Comentor
)
Files:
MAG_Hozjan_Zan_2025.pdf
(3,03 MB)
MD5: 53BB5234C9F7A949D2FF20669F3AE4A4
Language:
Slovenian
Work type:
Master's thesis/paper
Typology:
2.09 - Master's Thesis
Organization:
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:
V magistrskem delu predstavimo metodo za merjenje podobnosti metahevrističnih iskalnih strategij po vzorih iz narave. Predstavljena metoda s pomočjo meta-genetskega algoritma poišče nabor hiperparametrov metahevristik, ki povečuje podobnost vedenja metahevristik med reševanjem optimizacijskega problema. Metoda omogoča primerjavo metahevristik na podlagi metrik raznolikosti. Za merjenje podobnosti metahevristik uporabimo metrike podobnosti in modela strojnega učenja. Na koncu dela podamo rezultate in prediskutiramo ugotovitve.
Keywords:
metahevristične iskalne strategije po vzorih iz narave
,
primerjava metahevrističnih iskalnih strategij
,
genetski algoritem
Place of publishing:
Maribor
Place of performance:
Maribor
Publisher:
[Ž. Hozjan]
Year of publishing:
2025
Number of pages:
1 spletni vir (1 datoteka PDF (XIV, 59 str.))
PID:
20.500.12556/DKUM-93055
UDC:
[004.021:575.82]:004.832.2(043.2)
COBISS.SI-ID:
257238275
Publication date in DKUM:
17.10.2025
Views:
245
Downloads:
24
Metadata:
Categories:
KTFMB - FERI
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Licences
License:
CC BY 4.0, Creative Commons Attribution 4.0 International
Link:
http://creativecommons.org/licenses/by/4.0/
Description:
This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:
03.06.2025
Secondary language
Language:
English
Title:
Measuring the similarity of metaheuristic search strategies with machine learning models
Abstract:
In this thesis, we present a method for measuring the similarity of nature-inspired metaheuristic search strategies. The presented method uses a Meta-Genetic Algorithm to find a set of metaheuristics hyperparameters that maximise the similarity of the behaviour when solving an optimisation problem. The method allows comparison of metaheuristics based on diversity metrics. To measure the similarity of metaheuristics, we use similarity metrics and machine learning models. At the end of the thesis, we present the results and discuss the findings.
Keywords:
Nature-inspired metaheuristic search strategies
,
comparison of metaheuristic search strategies
,
genetic algorithm
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