Efficient Global Optimization using Deep Gaussian Processes
Optimization and Control
2018-09-14 v1 Machine Learning
Neural and Evolutionary Computing
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.
Cite
@article{arxiv.1809.04632,
title = {Efficient Global Optimization using Deep Gaussian Processes},
author = {Ali Hebbal and Loic Brevault and Mathieu Balesdent and El-Ghazali Talbi and Nouredine Melab},
journal= {arXiv preprint arXiv:1809.04632},
year = {2018}
}
Comments
12 pages, 11 Figures, 2 Tables, presented to the IEEE Congress on Evolutionary Computation (IEEE CEC 2018)