@article{RebolledoRehbachEibenetal.2020, author = {Margarita Rebolledo and Frederik Rehbach and A.E. Eiben and Thomas Bartz-Beielstein}, title = {Parallelized Bayesian Optimization for Problems with Expensive Evaluation Functions}, url = {https://nbn-resolving.org/urn:nbn:de:hbz:832-cos4-9078}, year = {2020}, abstract = {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.}, language = {en} }