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Optimizing CMA-ES with CMA-ES
The performance of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is significantly affected by the selection of the specific CMA-ES variant and the parameter values used. Furthermore, optimal CMA-ES parameter configurations vary across different problem landscapes, making the task of tuning CMA-ES to a specific optimization problem a challenging mixed-integer optimization problem. In recent years, several advanced algorithms have been developed to address this problem, including the Sequential Model-based Algorithm Configuration (SMAC) and the Tree-structured Parzen Estimator (TPE).
In this study, we propose a novel approach for tuning CMA-ES by leveraging CMA-ES itself. Therefore, we combine the modular CMA-ES implementation with the margin extension to handle mixed-integer optimization problems. We show that CMA-ES can not only compete with SMAC and TPE but also outperform them in terms of wall clock time.
- All authors
- Thomaser, A.M.; Vogt, M.E.; Bäck, T.H.W.; Kononova, A.V.
- Editor(s)
- Stein, N. van; Marcelloni, F.; Lam, H.K.; Cottrell, M.; Filipe, J.
- Date
- 2023
- Title of host publication
- 15th International Conference on Evolutionary Computation Theory and Applications
- Pages
- 214–221
- ISBN
- 9789897586743