Neural-Network-Based Estimation of Normal Distributions in Black-Box Optimization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F22%3A00360763" target="_blank" >RIV/68407700:21240/22:00360763 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11320/22:10450929 RIV/68407700:21340/22:00360763

Výsledek na webu

<a href="https://doi.org/10.14428/esann/2022.ES2022-113" target="_blank" >https://doi.org/10.14428/esann/2022.ES2022-113</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14428/esann/2022.ES2022-113" target="_blank" >10.14428/esann/2022.ES2022-113</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Neural-Network-Based Estimation of Normal Distributions in Black-Box Optimization

Popis výsledku v původním jazyce

The paper presents a novel application of artificial neural networks (ANNs) in the context of surrogate models for black-box optimization, i.e. optimization of objective functions that are accessed through empirical evaluation. For active learning of surrogate models, a very important role plays learning of multidimensional normal distributions, for which Gaussian processes (GPs) have been traditionally used. On the other hand, the research reported in this paper evaluated the applicability of two ANN-based methods to this end: combining GPs with ANNs and learning normal distributions with evidential ANNs. After methods sketch, the paper brings their comparison on a large collection of data from surrogate-assisted black-box optimization. It shows that combining GPs using linear covariance functions with ANNs yields lower errors than the investigated methods of evidential learning.

Název v anglickém jazyce

Neural-Network-Based Estimation of Normal Distributions in Black-Box Optimization

Popis výsledku anglicky

The paper presents a novel application of artificial neural networks (ANNs) in the context of surrogate models for black-box optimization, i.e. optimization of objective functions that are accessed through empirical evaluation. For active learning of surrogate models, a very important role plays learning of multidimensional normal distributions, for which Gaussian processes (GPs) have been traditionally used. On the other hand, the research reported in this paper evaluated the applicability of two ANN-based methods to this end: combining GPs with ANNs and learning normal distributions with evidential ANNs. After methods sketch, the paper brings their comparison on a large collection of data from surrogate-assisted black-box optimization. It shows that combining GPs using linear covariance functions with ANNs yields lower errors than the investigated methods of evidential learning.

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/LM2018131" target="_blank" >LM2018131: Česká národní infrastruktura pro biologická data</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

ESANN 2022 proceedings

ISBN

978-2-87587-084-1

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

187-192

Název nakladatele

Ciaco - i6doc.com

Místo vydání

Louvain la Neuve

Místo konání akce

Bruges

Datum konání akce

5. 10. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—