Towards Landscape Analysis in Adaptive Learning of Surrogate Models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F20%3A00536612" target="_blank" >RIV/67985807:_____/20:00536612 - isvavai.cz</a>
Result on the web
<a href="http://ceur-ws.org/Vol-2660/ialatecml_shortpaper2.pdf" target="_blank" >http://ceur-ws.org/Vol-2660/ialatecml_shortpaper2.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Towards Landscape Analysis in Adaptive Learning of Surrogate Models
Original language description
A context in which we expect adaptive learning to be promising is the choice of a suitable optimization strategy in black-box optimization. The reason why strategy adaptation is needed in such a situation is that knowledge of the blackbox objective function is obtained only gradually during the optimization. That knowledge covers two aspects: 1. the landscape of the black-box objective, revealed through its evaluation in previous iterations./ 2. success or failure of the optimization strategies applied to that black-box objective in previous iterations. To extract landscape knowledge, landscape analysis has been developed during the last decade. To include also the second aspect, we complement features obtained using the landscape analysis with features describing the optimization employed in previous iterations. Our interest is in expensive black-box optimization, where the number of evaluations of the expensive objective is usually decreased using a suitable surrogate model. Therefore, the research reported in this extended abstract addresses adaptive learning of surrogate models, more precisely their learning in surrogateassisted versions of the state-of-the-art black-box optimization method, Covariance Matrix Adaptation Evolution Strategy (CMA-ES).
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/GA18-18080S" target="_blank" >GA18-18080S: Fusion-Based Knowledge Discovery in Human Activity Data</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
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 Workshop on Interactive Adaptive Learning
ISBN
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ISSN
1613-0073
e-ISSN
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Number of pages
6
Pages from-to
78-83
Publisher name
Technical University & CreateSpace Independent Publishing
Place of publication
Aachen
Event location
Virtual Ghent
Event date
Sep 14, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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