Quantum inspired automatic clustering algorithms: A comparative study of genetic algorithm and bat algorithm
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10246920" target="_blank" >RIV/61989100:27240/20:10246920 - isvavai.cz</a>
Result on the web
<a href="https://www.degruyter.com/document/doi/10.1515/9783110670707/html" target="_blank" >https://www.degruyter.com/document/doi/10.1515/9783110670707/html</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1515/9783110670707-005" target="_blank" >10.1515/9783110670707-005</a>
Alternative languages
Result language
angličtina
Original language name
Quantum inspired automatic clustering algorithms: A comparative study of genetic algorithm and bat algorithm
Original language description
This article is intendant to present two automatic clustering techniques of image datasets, based on quantum inspired framework with two different metaheuristic algorithms, viz., Genetic Algorithm (GA) and Bat Algorithm (BA). This work provides two novel techniques to automatically find out the optimum clusters present in images and also provides a comparative study between the Quantum Inspired Genetic Algorithm (QIGA) and Quantum Inspired Bat Algorithm (QIBA). A comparison is also presented between these quantum inspired algorithms with their analogous classical counterparts. During the experiment, it was perceived that the quantum inspired techniques beat their classical techniques. The comparison was prepared based on the mean values of the fitness, standard deviation, standard error of the computed fitness of the cluster validity index and the optimal computational time. Finally, the supremacy of the algorithms was verified in terms of the p-value which was computed by t-test (statistical superiority test) and ranking of the proposed procedures was produced by the Friedman test. During the computation, the betterment of the fitness was judge by a well-known cluster validity index, named, DB index. The experiments were carried out on four Berkeley image and two real life grey scale images. (C) 2020 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved.
Czech name
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Czech description
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Classification
Type
C - Chapter in a specialist book
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
Quantum Machine Learning
ISBN
978-3-11-067070-7
Number of pages of the result
26
Pages from-to
89-114
Number of pages of the book
131
Publisher name
De Gruyter
Place of publication
Berlin
UT code for WoS chapter
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