HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

Efficient Global Optimization using Deep Gaussian Processes

Abstract : 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.
Complete list of metadata

Cited literature [22 references]  Display  Hide  Download

Contributor : Ali Hebbal Connect in order to contact the contributor
Submitted on : Monday, August 31, 2020 - 11:17:47 AM
Last modification on : Wednesday, March 23, 2022 - 3:51:26 PM
Long-term archiving on: : Tuesday, December 1, 2020 - 12:04:02 PM


Files produced by the author(s)


  • HAL Id : hal-01919795, version 1


Ali Hebbal, Loïc Brevault, Mathieu Balesdent, El-Ghazali Talbi, Nouredine Melab. Efficient Global Optimization using Deep Gaussian Processes. CEC 2018 - Congress on Evolutionary Computation, Jul 2018, Rio de Janeiro, Brazil. ⟨hal-01919795⟩



Record views


Files downloads