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Measuring performance of optimization algorithms in evolutionary computation
International Journal of Machine Learning and Computing, 6 (2016), 3; 167-171. https://doi.org/10.18178/ijmlc.2016.6.3.593
Ivković, Nikola; Jakobović, Domagoj; Golub, Marin

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Ivković, N., Jakobović, D. i Golub, M. (2016). Measuring performance of optimization algorithms in evolutionary computation. International Journal of Machine Learning and Computing, 6. (3), 167-171. doi: 10.18178/ijmlc.2016.6.3.593

Ivković, Nikola, et al. "Measuring performance of optimization algorithms in evolutionary computation." International Journal of Machine Learning and Computing, vol. 6, br. 3, 2016, str. 167-171. https://doi.org/10.18178/ijmlc.2016.6.3.593

Ivković, Nikola, Domagoj Jakobović i Marin Golub. "Measuring performance of optimization algorithms in evolutionary computation." International Journal of Machine Learning and Computing 6, br. 3 (2016): 167-171. https://doi.org/10.18178/ijmlc.2016.6.3.593

Ivković, N., Jakobović, D. i Golub, M. (2016) 'Measuring performance of optimization algorithms in evolutionary computation', International Journal of Machine Learning and Computing, 6(3), str. 167-171. doi: 10.18178/ijmlc.2016.6.3.593

Ivković N, Jakobović D, Golub M. Measuring performance of optimization algorithms in evolutionary computation. International Journal of Machine Learning and Computing [Internet]. 2016. [pristupljeno 16.04.2024.];6(3):167-171. doi: 10.18178/ijmlc.2016.6.3.593

N. Ivković, D. Jakobović i M. Golub, "Measuring performance of optimization algorithms in evolutionary computation", International Journal of Machine Learning and Computing, vol. 6, br. 3, str. 167-171, 2016. [Online]. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:211:418448. [Citirano: 16.04.2024.]

Za citiranje koristite ovu mrežnu adresu: https://urn.nsk.hr/urn:nbn:hr:211:418448
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Podaci o radu
Naslov (engleski)Measuring performance of optimization algorithms in evolutionary computation
AutorNikola Ivković
AutorDomagoj Jakobović
AutorMarin Golub
Autorova ustanovaSveučilište u Zagrebu
Fakultet organizacije i informatike
Znanstveno / umjetničko
područje, polje i grana		DRUŠTVENE ZNANOSTI
Informacijske i komunikacijske znanosti
Sažetak (engleski)
Reporting the results of optimization algorithms in evolutionary computation is a challenging task with many potential pitfalls. The source of problems is their stochastic nature and inability to guarantee an optimal solution in polynomial time. One of the basic questions that is often not addressed concerns the method of summarizing the entire distribution of solutions into a single value. Although the mean value is used by default for that purpose, the best solution obtained is also occasionally used in addition to or instead of it. Based on our analysis of different possibilities for measuring the performance of stochastic optimization algorithms presented in this paper we propose quantiles as the standard measure of performance. Quantiles can be naturally interpreted for the designated purpose. Besides, they are defined even when the arithmetic mean is not, and are applicable in cases of multiple executions of an algorithm. Our study also showed that, on the contrary to many other fields, in the case of stochastic optimization algorithms the greater variability in measured data can be considered as an advantage.
Ključne riječi (engleski)
Jezikengleski
Vrsta publikacijeZnanstveni rad - Izvorni znanstveni rad
Status objaveObjavljen
Vrsta recenzijeRecenziran - međunarodna recenzija
Verzija publikacijeObjavljena verzija rada (izdavačev PDF)
Naslov časopisaInternational Journal of Machine Learning and Computing
Brojčani podacivol. 6, br. 3, str. 167-171
p-ISSN2010-3700
DOIhttps://doi.org/10.18178/ijmlc.2016.6.3.593
URN:NBNurn:nbn:hr:211:418448
Datum objave publikacije2016
URL dokumentahttp://www.ijmlc.org/vol6/593-A27.pdf
Vrsta resursaTekst
Prava pristupaOtvoreni pristup
Uvjeti korištenjaIkona licence Zaštićeno autorskim pravom.
Datum i vrijeme pohrane2017-10-04 07:02:51