# The Importance of Landscape Features for Performance Prediction of Modular CMA-ES Variants

arXiv: https://arxiv.org/abs/2204.07431 · HTML: https://mlsanity.com/paper/2204.07431/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 15 Apr 2022.

## Builds on (most cited first, 16 of 16)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting](https://mlsanity.com/paper/1603.08785/) (2016)
- [Automated Algorithm Selection: Survey and Perspectives](https://mlsanity.com/paper/1811.11597/) (2018)
- [Benchmarking in Optimization: Best Practice and Open Issues](https://mlsanity.com/paper/2007.03488/) (2020)
- [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)
- [Landscape-Aware Fixed-Budget Performance Regression and Algorithm Selection for Modular CMA-ES Variants](https://mlsanity.com/paper/2006.09855/) (2020)
- [Evolving the Structure of Evolution Strategies](https://mlsanity.com/paper/1610.05231/) (2016)
- [Online Selection of CMA-ES Variants](https://mlsanity.com/paper/1904.07801/) (2019)
- [The Impact of Hyper-Parameter Tuning for Landscape-Aware Performance Regression and Algorithm Selection](https://mlsanity.com/paper/2104.09272/) (2021)
- [OPTION: OPTImization Algorithm Benchmarking ONtology](https://mlsanity.com/paper/2104.11889/) (2021)
- [Explorative Data Analysis of Time Series based AlgorithmFeatures of CMA-ES Variants](https://mlsanity.com/paper/2104.08098/) (2021)
- [A Modular Hybridization of Particle Swarm Optimization and Differential Evolution](https://mlsanity.com/paper/2006.11886/) (2020)
- [Explainable Landscape-Aware Optimization Performance Prediction](https://mlsanity.com/paper/2110.11633/) (2021)
- [Benchmarking a (μ+λ) Genetic Algorithm with Configurable Crossover Probability](https://mlsanity.com/paper/2006.05889/) (2020)
- [Towards Large Scale Automated Algorithm Design by Integrating Modular Benchmarking Frameworks](https://mlsanity.com/paper/2102.06435/) (2021)

## Built on by (most cited first, 5 of 5)

- [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)
- [A Survey of Features Used for Representing Black-box Single-objective Continuous Optimization](https://mlsanity.com/paper/2406.06629/) (2024)
- [Explainable Information Processing in Particle Swarm Optimization through Landscape and Search Behavior Analysis](https://mlsanity.com/paper/2509.06272/) (2025)
- [Quantifying the Impact of Modules and Their Interactions in the PSO-X Framework](https://mlsanity.com/paper/2601.04100/) (2026)

## Abstract (arXiv)

Selecting the most suitable algorithm and determining its hyperparameters for a given optimization problem is a challenging task. Accurately predicting how well a certain algorithm could solve the problem is hence desirable. Recent studies in single-objective numerical optimization show that supervised machine learning methods can predict algorithm performance using landscape features extracted from the problem instances. Existing approaches typically treat the algorithms as black-boxes, without consideration of their characteristics. To investigate in this work if a selection of landscape features that depends on algorithms properties could further improve regression accuracy, we regard the modular CMA-ES framework and estimate how much each landscape feature contributes to the best algorithm performance regression models. Exploratory data analysis performed on this data indicate that the set of most relevant features does not depend on the configuration of individual modules, but the influence that these features have on regression accuracy does. In addition, we have shown that by using classifiers that take the features relevance on the model accuracy, we are able to predict the status of individual modules in the CMA-ES configurations.