Improving Optimization With Gaussian Processes in the Covariance Matrix Adaptation Evolution Strategy
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F23%3A00579713" target="_blank" >RIV/67985807:_____/23:00579713 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21240/23:00368599
Result on the web
<a href="https://ceur-ws.org/Vol-3498/paper10.pdf" target="_blank" >https://ceur-ws.org/Vol-3498/paper10.pdf</a>
DOI - Digital Object Identifier
—
Alternative languages
Result language
angličtina
Original language name
Improving Optimization With Gaussian Processes in the Covariance Matrix Adaptation Evolution Strategy
Original language description
This paper explores the use of Gaussian processes (GPs) in the covariance matrix adaptation evolution strategy (CMA-ES) for black-box optimization. GPs are powerful probabilistic models that capture complex relationships, making them suitable for modeling uncertain objective functions. Integrating GPs into the CMA-ES improves exploration and adaptation in the search space, enhancing convergence speed and solution quality. The paper describes a novel implementation framework allowing to use GPs as surrogate models for the CMA-ES. That framework findings encourage further research to advance the application of GPs in black-box optimization.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 23st Conference Information Technologies – Applications and Theory (ITAT 2023)
ISBN
—
ISSN
1613-0073
e-ISSN
—
Number of pages
7
Pages from-to
82-88
Publisher name
Technical University & CreateSpace Independent Publishing
Place of publication
Aachen
Event location
Tatranské Matliare
Event date
Sep 22, 2023
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
—