Tracing the Interactions of Modular CMA-ES Configurations Across Problem Landscapes
This work provides incremental insights for researchers and practitioners in evolutionary computation by analyzing algorithm interactions with problem landscapes.
The paper investigated how different configurations of the modular CMA-ES algorithm perform across various benchmark problems, revealing shared and distinct behavioral patterns influenced by problem features to enhance interpretability and guide configuration choices.
This paper leverages the recently introduced concept of algorithm footprints to investigate the interplay between algorithm configurations and problem characteristics. Performance footprints are calculated for six modular variants of the CMA-ES algorithm (modCMA), evaluated on 24 benchmark problems from the BBOB suite, across two-dimensional settings: 5-dimensional and 30-dimensional. These footprints provide insights into why different configurations of the same algorithm exhibit varying performance and identify the problem features influencing these outcomes. Our analysis uncovers shared behavioral patterns across configurations due to common interactions with problem properties, as well as distinct behaviors on the same problem driven by differing problem features. The results demonstrate the effectiveness of algorithm footprints in enhancing interpretability and guiding configuration choices.