TY  - RPRT
A1  - Rebolledo, Margarita
A1  - Rehbach, Frederik
A1  - Eiben, A.E.
A1  - Bartz-Beielstein, Thomas
T1  - Parallelized Bayesian Optimization for Expensive Robot Controller Evolution
N2  - An important class of black-box optimization problems relies on using simulations to assess the quality of a given candidate solution. Solving such problems can be computationally expensive because each simulation is very time-consuming. We present an approach to mitigate this problem by distinguishing two factors of computational cost: the number of trials and the time needed to execute the trials. Our approach tries to keep down the number of trials by using Bayesian optimization (BO) –known to be sample efficient– and reducing wall-clock times by parallel execution of trials. We compare the performance of four parallelization methods and two model-free alternatives. Each method is evaluated on all 24 objective functions of the Black-Box-Optimization- Benchmarking (BBOB) test suite in their five, ten, and 20-dimensional versions. Additionally, their performance is investigated on six test cases in robot learning. The results show that parallelized BO outperforms the state-of-the-art CMA-ES on the BBOB test functions, especially for higher dimensions. On the robot learning tasks, the differences are less clear, but the data do support parallelized BO as the ‘best guess’, winning on some cases and never losing.
T3  - CIplus - 7/2020 
KW  - Optimierung
KW  - Benchmarking
KW  - Parallelisierung
KW  - Bayesian Optimization
KW  - Parallelization
KW  - Robotics
KW  - Benchmarking
Y1  - 
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos4-9130
ER  - 
TY  - JOUR
A1  - Rebolledo, Margarita
A1  - Rehbach, Frederik
A1  - Eiben, A.E.
A1  - Bartz-Beielstein, Thomas
T1  - Parallelized Bayesian Optimization for Problems with Expensive Evaluation Functions
N2  - 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.
T3  - CIplus - 6/2020 
KW  - Optimierung
KW  - Benchmarking
KW  - Bayesian Optimization
KW  - Benchmarking
KW  - BBOB
Y1  - 
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos4-9078
ER  -