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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

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

<a href="/cs/project/GA18-18080S" target="_blank" >GA18-18080S: Objevování znalostí v datech o aktivitě člověka založené na fúzi</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

Learning and Intelligent Optimization. Revised Selected Papers

ISBN

978-3-030-92120-0

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

16

Strana od-do

167-182

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Athens / online

Datum konání akce

20. 6. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000922798500015