Adaptive Generation-Based Evolution Control for Gaussian Process Surrogate Models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F17%3A00478631" target="_blank" >RIV/67985807:_____/17:00478631 - isvavai.cz</a>
Result on the web
<a href="http://ceur-ws.org/Vol-1885/136.pdf" target="_blank" >http://ceur-ws.org/Vol-1885/136.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Adaptive Generation-Based Evolution Control for Gaussian Process Surrogate Models
Original language description
The interest in accelerating black-box optimizers has resulted in several surrogate model-assisted version of the Covariance Matrix Adaptation Evolution Strategy, a state-of-the-art continuous black-box optimizer. The version called Surrogate CMA-ES uses Gaussian processes or random forests surrogate models with a generation-based evolution control. This paper presents an adaptive improvement for S-CMA-ES, in which the number of generations using the surrogate model before retraining is adjusted depending on the performance of the last instance of the surrogate. Three algorithms that differ in the measure of the surrogate model’s performance are evaluated on the COCO/BBOB framework. The results show a minor improvement on S-CMA-ES with constant model lifelengths, especially when larger lifelengths are considered.
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/GA17-01251S" target="_blank" >GA17-01251S: Metalearning for Extraction of Rules with Numerical Consequents</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2017
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 ITAT 2017: Information Technologies - Applications and Theory
ISBN
978-1974274741
ISSN
1613-0073
e-ISSN
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Number of pages
8
Pages from-to
136-143
Publisher name
Technical University & CreateSpace Independent Publishing Platform
Place of publication
Aachen & Charleston
Event location
Martinské hole
Event date
Sep 22, 2017
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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