Comparing SVM, Gaussian Process and Random Forest Surrogate Models for the CMA-ES

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F15%3A00447920" target="_blank" >RIV/67985807:_____/15:00447920 - 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

Comparing SVM, Gaussian Process and Random Forest Surrogate Models for the CMA-ES

Popis výsledku v původním jazyce

In practical optimization tasks, it is more and more frequent that the objective function is black-box which means that it cannot be described mathematically. Such functions can be evaluated only empirically, usually through some costly or time-consumingmeasurement, numerical simulation or experimental testing. Therefore, an important direction of research is the approximation of these objective functions with a suitable regression model, also called surrogate model of the objective functions. This paper evaluates two different approaches to the continuous black-box optimization which both integrates surrogate models with the state-of-the-art optimizer CMAES. The first Ranking SVM surrogate model estimates the ordering of the sampled points as the CMA-ES utilizes only the ranking of the fitness values. However, we show that continuous Gaussian processes model provides in the early states of the optimization comparable results.

Název v anglickém jazyce

Comparing SVM, Gaussian Process and Random Forest Surrogate Models for the CMA-ES

Popis výsledku anglicky

In practical optimization tasks, it is more and more frequent that the objective function is black-box which means that it cannot be described mathematically. Such functions can be evaluated only empirically, usually through some costly or time-consumingmeasurement, numerical simulation or experimental testing. Therefore, an important direction of research is the approximation of these objective functions with a suitable regression model, also called surrogate model of the objective functions. This paper evaluates two different approaches to the continuous black-box optimization which both integrates surrogate models with the state-of-the-art optimizer CMAES. The first Ranking SVM surrogate model estimates the ordering of the sampled points as the CMA-ES utilizes only the ranking of the fitness values. However, we show that continuous Gaussian processes model provides in the early states of the optimization comparable results.

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

186-193

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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