Knowledge-based Selection of Gaussian Process Surrogates

Result code in 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>

Alternative codes found

RIV/68407700:21340/19:00334847

Result on the web

<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

—

Result language

angličtina

Original language name

Knowledge-based Selection of Gaussian Process Surrogates

Original language description

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.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2019

Confidentiality

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

Article name in the collection

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

ISBN

—

ISSN

1613-0073

e-ISSN

—

Number of pages

16

Pages from-to

48-63

Publisher name

Technical University & CreateSpace Independent Publishing Platform

Place of publication

Aachen

Event location

Würzburg

Event date

Sep 16, 2019

Type of event by nationality

EUR - Evropská akce

UT code for WoS article

—