Differential evolution for the optimization of low-discrepancy generalized Halton sequences
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10244661" target="_blank" >RIV/61989100:27240/20:10244661 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2210650219302688?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2210650219302688?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.swevo.2020.100649" target="_blank" >10.1016/j.swevo.2020.100649</a>
Alternative languages
Result language
angličtina
Original language name
Differential evolution for the optimization of low-discrepancy generalized Halton sequences
Original language description
Halton sequences are d-dimensional quasirandom sequences that fill the d-dimensional hyperspace in a uniform way. They can be used in a variety of applications such as multidimensional integration, uniform sampling, and, e.g., quasi-Monte Carlo simulations. Generalized Halton sequences improve the space-filling properties of original Halton sequences in higher dimensions by digit scrambling. In this work, an evolutionary optimization algorithm, the differential evolution, is used to optimize scrambling permutations of a d-dimensional generalized Halton sequence so that the discrepancy of the generated point set is minimized. (C) 2020 Elsevier B.V.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
<a href="/en/project/EF17_049%2F0008425" target="_blank" >EF17_049/0008425: A Research Platform focused on Industry 4.0 and Robotics in Ostrava Agglomeration</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>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
Name of the periodical
Swarm and Evolutionary Computation
ISSN
2210-6502
e-ISSN
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Volume of the periodical
54
Issue of the periodical within the volume
100649
Country of publishing house
US - UNITED STATES
Number of pages
16
Pages from-to
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UT code for WoS article
000528484400009
EID of the result in the Scopus database
2-s2.0-85079348365