Optimizing CMA-ES with CMA-ES

International Joint Conference on Computational Intelligence, pp. 214–221

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.

Authors 4

1. André Thomaser Aachen

   Leiden University · BMW (Germany) · BMW Group (Germany)

   Affiliation as printed

   BMW Group, Knorrstraße 147, Munich, Germany, --- Select a Country ---

   LIACS, Leiden University, Niels Bohrweg 1, Leiden, The Netherlands, --- Select a Country ---

2. Marc-Eric Vogt

   BMW (Germany) · BMW Group (Germany)

   Affiliation as printed

   BMW Group, Knorrstraße 147, Munich, Germany, --- Select a Country ---

3. Thomas Bäck Aachen

   Leiden University

   Affiliation as printed

   LIACS, Leiden University, Niels Bohrweg 1, Leiden, The Netherlands, --- Select a Country ---

4. Anna V. Kononova Aachen

   Leiden University

   Affiliation as printed

   LIACS, Leiden University, Niels Bohrweg 1, Leiden, The Netherlands, --- Select a Country ---