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.
Author: | Margarita Rebolledo, Frederik Rehbach, A.E. Eiben, Thomas Bartz-Beielstein |
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URN: | urn:nbn:de:hbz:832-cos4-9078 |
Series (Serial Number): | CIplus (6/2020) |
Document Type: | Article |
Language: | English |
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): | Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International |