Refine
Document Type
- Article (2)
Language
- English (2) (remove)
Has Fulltext
- yes (2)
Keywords
- BBOB (1)
- Bayesian Optimization (1)
- Benchmarking (1)
- Evolutionary Computation (1)
- Evolutionärer Algorithmus (1)
- Globale Optimierung (1)
- Metaheuristics (1)
- Metaheuristik (1)
- Optimierung (1)
- Optimization (1)
- Surrogate (1)
- Taxonomie (1)
- Taxonomy (1)
Institute
- Fakultät für Informatik und Ingenieurwissenschaften (F10) (2) (remove)
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.