International Workshop on Machine Learning, Optimization and Big Data

Machine Learning, Optimization, and Big Data pp 185-196 | Cite as

Global Optimization with Sparse and Local Gaussian Process Models

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9432)

Abstract

We present a novel surrogate model-based global optimization framework allowing a large number of function evaluations. The method, called SpLEGO, is based on a multi-scale expected improvement (EI) framework relying on both sparse and local Gaussian process (GP) models. First, a bi-objective approach relying on a global sparse GP model is used to determine potential next sampling regions. Local GP models are then constructed within each selected region. The method subsequently employs the standard expected improvement criterion to deal with the exploration-exploitation trade-off within selected local models, leading to a decision on where to perform the next function evaluation(s). The potential of our approach is demonstrated using the so-called Sparse Pseudo-input GP as a global model. The algorithm is tested on four benchmark problems, whose number of starting points ranges from \(10^{2}\) to \(10^{4}\). Our results show that SpLEGO is effective and capable of solving problems with large number of starting points, and it even provides significant advantages when compared with state-of-the-art EI algorithms.

Keywords

Black-box optimization Expected improvement Kriging 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Mathematics and Statistics, IMSVUniversity of BernBernSwitzerland
  2. 2.Idiap Research InstituteMartignySwitzerland

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