# Online Selection of CMA-ES Variants

## TL;DR

This paper develops and tests an adaptive CMA-ES variant selection method, improving performance on benchmark functions by dynamically switching between algorithm modules, with insights into module importance.

## Contribution

It proposes a revised, more reliable adaptive selection approach for CMA-ES variants, demonstrating performance improvements and analyzing module activation patterns.

## Key findings

- Performance gains on 18 of 24 benchmark functions
- Stable advantages of up to 23% over baseline
- Identification of crucial modules for different optimization phases

## Abstract

In the field of evolutionary computation, one of the most challenging topics is algorithm selection. Knowing which heuristics to use for which optimization problem is key to obtaining high-quality solutions. We aim to extend this research topic by taking a first step towards a selection method for adaptive CMA-ES algorithms. We build upon the theoretical work done by van Rijn \textit{et al.} [PPSN'18], in which the potential of switching between different CMA-ES variants was quantified in the context of a modular CMA-ES framework. We demonstrate in this work that their proposed approach is not very reliable, in that implementing the suggested adaptive configurations does not yield the predicted performance gains. We propose a revised approach, which results in a more robust fit between predicted and actual performance. The adaptive CMA-ES approach obtains performance gains on 18 out of 24 tested functions of the BBOB benchmark, with stable advantages of up to 23\%. An analysis of module activation indicates which modules are most crucial for the different phases of optimizing each of the 24 benchmark problems. The module activation also suggests that additional gains are possible when including the (B)IPOP modules, which we have excluded for this present work.