Model Guided Sampling Optimization for Low-Dimensional Problems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F15%3A00439763" target="_blank" >RIV/67985807:_____/15:00439763 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Model Guided Sampling Optimization for Low-Dimensional Problems
Original language description
Optimization of very expensive black-box functions requires utilization of maximum information gathered by the process of optimization. Model Guided Sampling Optimization (MGSO) forms a more robust alternative to Jones? Gaussian-process-based EGO algorithm. Instead of EGO?s maximizing expected improvement, the MGSO uses sampling the probability of improvement which is shown to be helpful against trapping in local minima. Further, the MGSO can reach close-to-optimum solutions faster than standard optimization algorithms on low dimensional or smooth problems.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ICAART 2015. Proceedings of the International Conference on Agents and Artificial Intelligence, Volume 2
ISBN
978-989-758-074-1
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
451-456
Publisher name
Scitepress
Place of publication
Lisbon
Event location
Lisbon
Event date
Jan 10, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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