Neural-Network-Based Estimation of Normal Distributions in Black-Box Optimization
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F22%3A00360763" target="_blank" >RIV/68407700:21240/22:00360763 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11320/22:10450929 RIV/68407700:21340/22:00360763
Result on the web
<a href="https://doi.org/10.14428/esann/2022.ES2022-113" target="_blank" >https://doi.org/10.14428/esann/2022.ES2022-113</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.14428/esann/2022.ES2022-113" target="_blank" >10.14428/esann/2022.ES2022-113</a>
Alternative languages
Result language
angličtina
Original language name
Neural-Network-Based Estimation of Normal Distributions in Black-Box Optimization
Original language description
The paper presents a novel application of artificial neural networks (ANNs) in the context of surrogate models for black-box optimization, i.e. optimization of objective functions that are accessed through empirical evaluation. For active learning of surrogate models, a very important role plays learning of multidimensional normal distributions, for which Gaussian processes (GPs) have been traditionally used. On the other hand, the research reported in this paper evaluated the applicability of two ANN-based methods to this end: combining GPs with ANNs and learning normal distributions with evidential ANNs. After methods sketch, the paper brings their comparison on a large collection of data from surrogate-assisted black-box optimization. It shows that combining GPs using linear covariance functions with ANNs yields lower errors than the investigated methods of evidential learning.
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/LM2018131" target="_blank" >LM2018131: Czech National Infrastructure for Biological Data</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ESANN 2022 proceedings
ISBN
978-2-87587-084-1
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
187-192
Publisher name
Ciaco - i6doc.com
Place of publication
Louvain la Neuve
Event location
Bruges
Event date
Oct 5, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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