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Parallelized Bayesian Optimization for Problems with Expensive Evaluation Functions

  • Many black-box optimization problems rely on simulations to evaluate the quality of candidate solutions. These evaluations can be computationally expensive and very time-consuming. We present and approach to mitigate this problem by taking into consideration two factors: The number of evaluations and the execution time. We aim to keep the number of evaluations low by using Bayesian optimization (BO) – known to be sample efficient– and to reduce wall-clock times by executing parallel evaluations. Four parallelization methods using BO as optimizer are compared against the inherently parallel CMA-ES. Each method is evaluated on all the 24 objective functions of the Black-Box-Optimization-Benchmarking test suite in their 20-dimensional versions. The results show that parallelized BO outperforms the state-of-the-art CMA-ES on most of the test functions, also on higher dimensions.

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Author:Margarita Rebolledo, Frederik Rehbach, A.E. Eiben, Thomas Bartz-Beielstein
Series (Serial Number):CIplus (6/2020)
Document Type:Article
Release Date:2020/08/05
Tag:BBOB; Bayesian Optimization; Benchmarking
GND Keyword:Optimierung; Benchmarking
Page Number:2
Descirption of the primary publication:GECCO '20: Genetic and Evolutionary Computation Conference, Cancún Mexico, July, 2020
Institutes and Central Facilities:Fakultät für Informatik und Ingenieurwissenschaften (F10)
CCS-Classification:G. Mathematics of Computing
Dewey Decimal Classification:000 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik
JEL-Classification:C Mathematical and Quantitative Methods
Open Access:Open Access
Licence (German):License LogoCreative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International