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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.
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Contributor : Ali Hebbal <>
Submitted on : Monday, August 31, 2020 - 11:17:47 AM
Last modification on : Wednesday, April 14, 2021 - 3:37:04 AM
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  • 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⟩



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