Revisiting Bayesian Optimization in the light of the COCO benchmark

Autor: Victor Picheny, Rodolphe Le Riche
Přispěvatelé: Centre National de la Recherche Scientifique (CNRS), Institut Henri Fayol (FAYOL-ENSMSE), École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Département Génie mathématique et industriel (FAYOL-ENSMSE), Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE)-Institut Henri Fayol, Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS), Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Institut national polytechnique Clermont Auvergne (INP Clermont Auvergne), Université Clermont Auvergne (UCA)-Université Clermont Auvergne (UCA), Institut Mines-Télécom [Paris] (IMT), Université Clermont Auvergne (UCA), Secondmind, Ecole Nationale Supérieure des Mines de St Etienne-Institut Henri Fayol, Ecole Nationale Supérieure des Mines de St Etienne-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Institut national polytechnique Clermont Auvergne (INP Clermont Auvergne)
Jazyk: angličtina
Rok vydání: 2021
Předmět:
FOS: Computer and information sciences
Mathematical optimization
Control and Optimization
Computer science
0211 other engineering and technologies
Machine Learning (stat.ML)
02 engineering and technology
Statistics - Computation
symbols.namesake
Statistics - Machine Learning
0202 electrical engineering
electronic engineering
information engineering
Code (cryptography)
FOS: Mathematics
Optimization algorithm benchmark
Global optimization
Gaussian process
Mathematics - Optimization and Control
Computation (stat.CO)
Bayesian optimization
021103 operations research
global optimization
[INFO.INFO-CE]Computer Science [cs]/Computational Engineering
Finance
and Science [cs.CE]
Expensive function optimization
Function (mathematics)
Computer Graphics and Computer-Aided Design
[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation
Computer Science Applications
[SPI.MECA.GEME]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Mechanical engineering [physics.class-ph]
Control and Systems Engineering
Optimization and Control (math.OC)
Kernel (statistics)
symbols
Benchmark (computing)
020201 artificial intelligence & image processing
[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC]
Engineering design process
Software
Zdroj: Structural and Multidisciplinary Optimization
Structural and Multidisciplinary Optimization, 2021, ⟨10.1007/s00158-021-02977-1⟩
Structural and Multidisciplinary Optimization, Springer Verlag (Germany), 2021, ⟨10.1007/s00158-021-02977-1⟩
ISSN: 1615-147X
1615-1488
Popis: International audience; It is commonly believed that Bayesian optimization (BO) algorithms are highly efficient for optimizing numerically costly functions. However, BO is not often compared to widely different alternatives, and is mostly tested on narrow sets of problems (multimodal, low-dimensional functions), which makes it difficult to assess where (or if) they actually achieve state-of-the-art performance. Moreover, several aspects in the design of these algorithms vary across implementations without a clear recommendation emerging from current practices, and many of these design choices are not substantiated by authoritative test campaigns. This article reports a large investigation about the effects on the performance of (Gaussian process based) BO of common and less common design choices. The experiments are carried out with the established COCO (COmparing Continuous Optimizers) software. It is found that a small initial budget, a quadratic trend, high-quality optimization of the acquisition criterion bring consistent progress. Using the GP mean as an occasional acquisition contributes to a negligible additional improvement. Warping degrades performance. The Mat\'ern 5/2 kernel is a good default but it may be surpassed by the exponential kernel on irregular functions. Overall, the best EGO variants are competitive or improve over state-of-the-art algorithms in dimensions less or equal to 5 for multimodal functions. The code developed for this study makes the new version (v2.1.1) of the R package DiceOptim available on CRAN. The structure of the experiments by function groups allows to define priorities for future research on Bayesian optimization.
Databáze: OpenAIRE
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