Efficient Global Optimization using Deep Gaussian Processes

Hebbal, Ali; Brevault, Loïc; Balesdent, Mathieu; Talbi, El-Ghazali; Melab, Nouredine

Document type :

Communication dans un congrès avec actes

Title :

Efficient Global Optimization using Deep Gaussian Processes

Author(s) :

Hebbal, Ali [Auteur]
DTIS, ONERA, Université Paris Saclay (COmUE) [Palaiseau]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Optimisation de grande taille et calcul large échelle [BONUS]
Brevault, Loïc [Auteur]
DTIS, ONERA, Université Paris Saclay (COmUE) [Palaiseau]
Balesdent, Mathieu [Auteur]
DTIS, ONERA, Université Paris Saclay (COmUE) [Palaiseau]
Talbi, El-Ghazali [Auteur]

Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Optimisation de grande taille et calcul large échelle [BONUS]
Melab, Nouredine [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Optimisation de grande taille et calcul large échelle [BONUS]

Conference title :

CEC 2018 - Congress on Evolutionary Computation

City :

Rio de Janeiro

Country :

Brésil

Start date of the conference :

2018-07-08

English keyword(s) :

Efficient Global Optimization
Non-stationary Kriging
Deep Gaussian Processes

HAL domain(s) :

Statistiques [stat]/Machine Learning [stat.ML]
Informatique [cs]/Modélisation et simulation

English abstract : [en]

Efficient Global Optimization (EGO) is widely used for the optimization of computationally expensive black-box functions. It uses a surrogate modeling technique based on Gaussian Processes (Kriging). However, due to the ...
Show more >Efficient Global Optimization (EGO) is widely used for the optimization of computationally expensive black-box functions. It uses a surrogate modeling technique based on Gaussian Processes (Kriging). However, due to the use of a stationary covariance, Kriging is not well suited for approximating non stationary functions. This paper explores the integration of Deep Gaussian processes (DGP) in EGO framework to deal with the non-stationary issues and investigates the induced challenges and opportunities. Numerical experimentations are performed on analytical problems to highlight the different aspects of DGP and EGO.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

Files

https://hal.inria.fr/hal-01919795/file/conference_071817.pdf
Open access
Access the document

https://hal.inria.fr/hal-01919795/document
Open access
Access the document

https://hal.inria.fr/hal-01919795/document
Open access
Access the document

document
Open access
Access the document

conference_071817.pdf
Open access
Access the document

Efficient Global Optimization using Deep ... BibTeX CSV Excel RIS

Files

Efficient Global Optimization using Deep ...

BibTeX

CSV

Excel

RIS