Assessment of Surrogate Model Settings Using Landscape Analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F20%3A00533901" target="_blank" >RIV/67985807:_____/20:00533901 - isvavai.cz</a>

Výsledek na webu

<a href="http://ceur-ws.org/Vol-2718/paper20.pdf" target="_blank" >http://ceur-ws.org/Vol-2718/paper20.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Assessment of Surrogate Model Settings Using Landscape Analysis

Popis výsledku v původním jazyce

This work in progress concerns assessment of surrogate model settings for expensive black-box optimization. The assessment is performed in the context of Gaussian process models used in the Doubly Trained Surrogate (DTS) variant of the state-of-the-art black-box optimizer, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). This work focuses on the connection between Gaussian process surrogate model predictive accuracy and an essential model hyper-parameter – the covariance function. The performance of DTS-CMA-ES is related to the results of landscape analysis of the objective function. To this end various classification and regression methods are used, proposed in the traditional framework for algorithm selection by Rice. Several single-label classification, multi-label classification, and regression methods are experimentally evaluated on data from DTS-CMAES runs on the noiseless benchmark functions from the COCO platform for comparing continuous optimizers in black-box settings.n

Název v anglickém jazyce

Assessment of Surrogate Model Settings Using Landscape Analysis

Popis výsledku anglicky

This work in progress concerns assessment of surrogate model settings for expensive black-box optimization. The assessment is performed in the context of Gaussian process models used in the Doubly Trained Surrogate (DTS) variant of the state-of-the-art black-box optimizer, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). This work focuses on the connection between Gaussian process surrogate model predictive accuracy and an essential model hyper-parameter – the covariance function. The performance of DTS-CMA-ES is related to the results of landscape analysis of the objective function. To this end various classification and regression methods are used, proposed in the traditional framework for algorithm selection by Rice. Several single-label classification, multi-label classification, and regression methods are experimentally evaluated on data from DTS-CMAES runs on the noiseless benchmark functions from the COCO platform for comparing continuous optimizers in black-box settings.n

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA18-18080S" target="_blank" >GA18-18080S: Objevování znalostí v datech o aktivitě člověka založené na fúzi</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 20th Conference Information Technologies - Applications and Theory

ISBN

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ISSN

1613-0073

e-ISSN

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Počet stran výsledku

9

Strana od-do

81-89

Název nakladatele

Technical University & CreateSpace Independent Publishing

Místo vydání

Aachen

Místo konání akce

Oravská Lesná

Datum konání akce

18. 9. 2020

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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