@article{RebolledoRehbachEibenetal.2020,
  author    = {Rebolledo, Margarita and Rehbach, Frederik and Eiben, A.E. and Bartz-Beielstein, Thomas},
  title     = {Parallelized Bayesian Optimization for Problems with Expensive Evaluation Functions},
  institution = {Fakult{\"a}t f{\"u}r Informatik und Ingenieurwissenschaften (F10)},
  series    = {CIplus},
  number    = {6/2020},
  pages     = {2},
  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.},
  subject      = {Optimierung},
  language  = {en}
}