Authors:
André Thomaser
1
;
2
;
Marc-Eric Vogt
1
;
Thomas Bäck
2
and
Anna Kononova
2
Affiliations:
1
BMW Group, Knorrstraße 147, Munich, Germany
;
2
LIACS, Leiden University, Niels Bohrweg 1, Leiden, The Netherlands
Keyword(s):
Parameter Tuning, CMA-ES, Benchmarking, Mixed-Integer Optimization, TPE, SMAC, BBOB.
Abstract:
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.