Using Past Experience for Configuration of Gaussian Processes in Black-Box Optimization

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F21%3A00603035" target="_blank" >RIV/67985807:_____/21:00603035 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-030-92121-7_15" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-92121-7_15</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-92121-7_15" target="_blank" >10.1007/978-3-030-92121-7_15</a>

Result language

angličtina

Original language name

Using Past Experience for Configuration of Gaussian Processes in Black-Box Optimization

Original language description

This paper deals with the configuration of Gaussian processes serving as surrogate models in black-box optimization. It examines several different covariance functions of Gaussian processes (GPs) and a combination of GPs and artificial neural networks (ANNs). Different configurations are compared in the context of a surrogate-assisted version of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), a state-of-the-art evolutionary black-box optimizer. The configuration employs a new methodology, which consists of using data from past runs of the optimizer. In that way, it is possible to avoid demanding computations of the optimizer only to configure the surrogate model as well as to achieve a much more robust configuration relying on 4600 optimization runs in 5 different dimensions. The experimental part reveals that the lowest rank difference error, an error measure corresponding to the CMA-ES invariance with respect to monotonous transformations, is most often achieved using rational quadratic, squared exponential and Matérn 5/2 kernels. It also reveals that these three covariance functions are always equivalent, in the sense that the differences between their errors are never statistically significant. In some cases, they are also equivalent to other configurations, including the combination ANN-GP.

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

<a href="/en/project/GA18-18080S" target="_blank" >GA18-18080S: Fusion-Based Knowledge Discovery in Human Activity Data</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

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

Article name in the collection

Learning and Intelligent Optimization. Revised Selected Papers

ISBN

978-3-030-92120-0

ISSN

0302-9743

e-ISSN

—

Number of pages

16

Pages from-to

167-182

Publisher name

Springer

Place of publication

Cham

Event location

Athens / online

Event date

Jun 20, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000922798500015