Comparing Surrogate-Assisted Evolutionary Algorithms on Optimization of a Simulation Model for Resource Planning Task for Hospitals
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F24%3APU155646" target="_blank" >RIV/00216305:26210/24:PU155646 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10611951" target="_blank" >https://ieeexplore.ieee.org/document/10611951</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CEC60901.2024.10611951" target="_blank" >10.1109/CEC60901.2024.10611951</a>
Alternative languages
Result language
angličtina
Original language name
Comparing Surrogate-Assisted Evolutionary Algorithms on Optimization of a Simulation Model for Resource Planning Task for Hospitals
Original language description
Surrogate-assisted evolutionary algorithms (SAEAs) are currently among the most widely researched techniques for their capability to solve expensive real-world optimization problems. The development of these techniques and their bench-marking with other methods still relies almost exclusively on artificially created problems. In this paper, we use a real-world problem of optimizing the parameters of a hospital resource planning tool to compare the performance of nine state-of-the-art single-objective SAEAs. We find that there are significant differences between the performance of the compared methods on the selected instances, making the problems suitable for benchmarking SAEAs.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
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
<a href="/en/project/GA24-12474S" target="_blank" >GA24-12474S: Benchmarking derivative-free global optimization methods</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2024 IEEE Congress on Evolutionary Computation (CEC)
ISBN
979-8-3503-0836-5
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
„“-„“
Publisher name
IEEE
Place of publication
neuveden
Event location
Yokohama
Event date
Jun 30, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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