# Tracing the Interactions of Modular CMA-ES Configurations Across Problem Landscapes

arXiv: https://arxiv.org/abs/2507.02331 · HTML: https://mlsanity.com/paper/2507.02331/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 36 references that are in the map.
- First posted on arXiv 3 Jul 2025.

## Builds on (most cited first, 13 of 13)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [The many Shapley values for model explanation](https://mlsanity.com/paper/1908.08474/) (2019)
- [A Random Forest Guided Tour](https://mlsanity.com/paper/1511.05741/) (2015)
- [COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting](https://mlsanity.com/paper/1603.08785/) (2016)
- [Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy](https://mlsanity.com/paper/2006.11135/) (2020)
- [Tuning as a Means of Assessing the Benefits of New Ideas in Interplay with Existing Algorithmic Modules](https://mlsanity.com/paper/2102.12905/) (2021)
- [Modular Differential Evolution](https://mlsanity.com/paper/2304.09524/) (2023)
- [MA-BBOB: A Problem Generator for Black-Box Optimization Using Affine Combinations and Shifts](https://mlsanity.com/paper/2312.11083/) (2023)
- [Explainable Benchmarking for Iterative Optimization Heuristics](https://mlsanity.com/paper/2401.17842/) (2024)
- [PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization](https://mlsanity.com/paper/2310.10685/) (2023)
- [Quantifying Individual and Joint Module Impact in Modular Optimization Frameworks](https://mlsanity.com/paper/2405.11964/) (2024)
- [Algorithm Instance Footprint: Separating Easily Solvable and Challenging Problem Instances](https://mlsanity.com/paper/2306.00479/) (2023)

## Built on by (most cited first, 1 of 1)

- [Performance and Explainability Requirements of Evolutionary Algorithms in Real-World Physics-Informed Optimization](https://mlsanity.com/paper/2605.28164/) (2026)

## Abstract (arXiv)

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.