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Title:Implementacija algoritma klonske selekcije v Pythonu : diplomsko delo
Authors:ID Peršon, Andraž (Author)
ID Fister, Iztok (Mentor) More about this mentor... New window
ID Fister, Iztok (Comentor)
Files:.pdf UN_Person_Andraz_2024.pdf (1,05 MB)
MD5: D53256887ED9C0AFD28758B44B010123
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Namen diplomskega dela je implementirati algoritem, ki pri reševanju problemov uporablja vzore iz narave, podrobneje algoritem klonske selekcije. Celotna raziskava je bila izvedena na podlagi proučevanja spletnih virov. Omenjena sta programski jezik Python in knjižnica NiaPy, ki vključuje številne algoritme po vzorih iz narave. Predstavljene so rešitve, podobne algoritmu klonske selekcije, ki že obstajajo. Razložena sta algoritem klonske selekcije in njegova implementacija v programskem jeziku Python. Podrobno so predstavljeni rezultati in testiranje algoritma klonske selekcije ter integracija omenjenega algoritma v knjižnico NiaPy.
Keywords:algoritmi po vzoru iz narave, optimizacijski algoritem klonske selekcije, knjižnica NiaPy, Python
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[A. Peršon]
Year of publishing:2024
Number of pages:1 spletni vir (1 datoteka PDF (VIII, 33 f.))
PID:20.500.12556/DKUM-87019 New window
UDC:004.8.021(043.2)
COBISS.SI-ID:192308739 New window
Publication date in DKUM:01.03.2024
Views:652
Downloads:105
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:08.02.2024

Secondary language

Language:English
Title:Implementation of Clonal Selection algorithm in Python
Abstract:The purpose of this thesis is to implement a Clonal Selection algorithm that uses patterns from nature in solving problems. The entire research was conducted based on online resources. Similar existing solutions are introduced. The Python programming language as well as the NiaPy library, which includes a number of nature-inspired algorithms, are described. The Clonal Selection algorithm and its implementation in the Python programming language are defined. The results and testing of the Clonal Selection algorithm as well as the integration of mentioned algorithm into the NiaPy library are presented.
Keywords:nature-inspired algorithms, Clonal Selection optimization algorithm, library NiaPy, Python


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