Testing Gaussian Process Surrogates on CEC’2013 Multi-Modal Benchmark

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F16%3A00462910" target="_blank" >RIV/67985807:_____/16:00462910 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11320/16:10334233

Výsledek na webu

<a href="http://ceur-ws.org/Vol-1649/138.pdf" target="_blank" >http://ceur-ws.org/Vol-1649/138.pdf</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Testing Gaussian Process Surrogates on CEC’2013 Multi-Modal Benchmark

Popis výsledku v původním jazyce

This paper compares several Gaussian-processbased surrogate modeling methods applied to black-box optimization by means of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), which is considered state-of-the-art in the area of continuous black-box optimization. Among the compared methods are the Modelassisted CMA-ES, the Robust Kriging Metamodel CMAES, and the Surrogate CMA-ES. In addition, a very successful surrogate-assisted self-adaptive CMA-ES, which is not based on Gaussian processes, but on ordinary regression by means of support vector machines has been included into the comparison. Those methods have been benchmarked using CEC’2013 testing functions. We show that the surrogate CMA-ES achieves best results at the beginning and later phases of optimization process, conceding in the middle to surrogate-assisted CMA-ES.

Název v anglickém jazyce

Testing Gaussian Process Surrogates on CEC’2013 Multi-Modal Benchmark

Popis výsledku anglicky

This paper compares several Gaussian-processbased surrogate modeling methods applied to black-box optimization by means of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), which is considered state-of-the-art in the area of continuous black-box optimization. Among the compared methods are the Modelassisted CMA-ES, the Robust Kriging Metamodel CMAES, and the Surrogate CMA-ES. In addition, a very successful surrogate-assisted self-adaptive CMA-ES, which is not based on Gaussian processes, but on ordinary regression by means of support vector machines has been included into the comparison. Those methods have been benchmarked using CEC’2013 testing functions. We show that the surrogate CMA-ES achieves best results at the beginning and later phases of optimization process, conceding in the middle to surrogate-assisted CMA-ES.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings ITAT 2016: Information Technologies - Applications and Theory

ISBN

978-1-5370-1674-0

ISSN

1613-0073

e-ISSN

—

Počet stran výsledku

9

Strana od-do

138-146

Název nakladatele

Technical University & CreateSpace Independent Publishing Platform

Místo vydání

Aachen & Charleston

Místo konání akce

Tatranské Matliare

Datum konání akce

15. 9. 2016

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

—