Knowledge-based Selection of Gaussian Process Surrogates

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F19%3A00509320" target="_blank" >RIV/67985807:_____/19:00509320 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21340/19:00334847

Výsledek na webu

<a href="http://ceur-ws.org/Vol-2444/ialatecml_paper4.pdf" target="_blank" >http://ceur-ws.org/Vol-2444/ialatecml_paper4.pdf</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Knowledge-based Selection of Gaussian Process Surrogates

Popis výsledku v původním jazyce

Many real-world problems belong to the area of continuous black-box optimization. If the black-box function is also cost-aware, regression surrogate models are often utilized by optimization algorithms to save evaluations of the original cost-aware function. Choosing a suitable surrogate model or a suitable setting of its hyperparameters is a complex selection problem, where research into reusing knowledge represented by features of black-box function landscape is only starting. In this paper, we report the research into surrogate model selection, where knowledge from the previous experience with using the model is utilized to design a metalearing system. As a proof of concept, we provide a study investigating the influence of landscape features on the performance of various Gaussian process covariance functions as surrogate models for the state-of-the-art optimization algorithm in the cost-aware continuous black-box optimization.

Název v anglickém jazyce

Knowledge-based Selection of Gaussian Process Surrogates

Popis výsledku anglicky

Many real-world problems belong to the area of continuous black-box optimization. If the black-box function is also cost-aware, regression surrogate models are often utilized by optimization algorithms to save evaluations of the original cost-aware function. Choosing a suitable surrogate model or a suitable setting of its hyperparameters is a complex selection problem, where research into reusing knowledge represented by features of black-box function landscape is only starting. In this paper, we report the research into surrogate model selection, where knowledge from the previous experience with using the model is utilized to design a metalearing system. As a proof of concept, we provide a study investigating the influence of landscape features on the performance of various Gaussian process covariance functions as surrogate models for the state-of-the-art optimization algorithm in the cost-aware continuous black-box optimization.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

IAL ECML PKDD 2019: Workshop & Tutorial on Interactive Adaptive Learning. Proceedings

ISBN

—

ISSN

1613-0073

e-ISSN

—

Počet stran výsledku

16

Strana od-do

48-63

Název nakladatele

Technical University & CreateSpace Independent Publishing Platform

Místo vydání

Aachen

Místo konání akce

Würzburg

Datum konání akce

16. 9. 2019

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

—