Investigation of Gaussian Processes in the Context of Black-Box Evolutionary Optimization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F15%3A00447919" target="_blank" >RIV/67985807:_____/15:00447919 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Investigation of Gaussian Processes in the Context of Black-Box Evolutionary Optimization

Popis výsledku v původním jazyce

Minimizing the number of function evaluations became a very challenging problem in the field of blackbox optimization, when one evaluation of the objective function may be very expensive or time-consuming. Gaussian processes (GPs) are one of the approaches suggested to this end, already nearly 20 years ago, in the area of general global optimization. So far, however, they received only little attention in the area of evolutionary black-box optimization. This work investigates the performance of GPs in the context of black-box continuous optimization, using multimodal functions from the CEC 2013 competition. It shows the performance of two methods based on GPs, Model Guided Sampling Optimization (MGSO) and GPs as a surrogate model for CMA-ES. The papercompares the speed-up of both methods with respect to the number of function evaluations using different settings to CMAES with no surrogate model.

Název v anglickém jazyce

Investigation of Gaussian Processes in the Context of Black-Box Evolutionary Optimization

Popis výsledku anglicky

Minimizing the number of function evaluations became a very challenging problem in the field of blackbox optimization, when one evaluation of the objective function may be very expensive or time-consuming. Gaussian processes (GPs) are one of the approaches suggested to this end, already nearly 20 years ago, in the area of general global optimization. So far, however, they received only little attention in the area of evolutionary black-box optimization. This work investigates the performance of GPs in the context of black-box continuous optimization, using multimodal functions from the CEC 2013 competition. It shows the performance of two methods based on GPs, Model Guided Sampling Optimization (MGSO) and GPs as a surrogate model for CMA-ES. The papercompares the speed-up of both methods with respect to the number of function evaluations using different settings to CMAES with no surrogate model.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GA13-17187S" target="_blank" >GA13-17187S: Konstrukce pokročilých srozumitelných klasifikátorů</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings ITAT 2015: Information Technologies - Applications and Theory

ISBN

978-1-5151-2065-0

ISSN

1613-0073

e-ISSN

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Počet stran výsledku

8

Strana od-do

159-166

Název nakladatele

Technical University & CreateSpace Independent Publishing Platform

Místo vydání

Aachen & Charleston

Místo konání akce

Slovenský Raj

Datum konání akce

17. 9. 2015

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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