# Landscape-Aware Fixed-Budget Performance Regression and Algorithm Selection for Modular CMA-ES Variants

arXiv: https://arxiv.org/abs/2006.09855 · HTML: https://mlsanity.com/paper/2006.09855/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2020.

## Built on by (most cited first, 15 of 15)

- [Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions](https://mlsanity.com/paper/2102.00736/) (2021)
- [DoE2Vec: Deep-learning Based Features for Exploratory Landscape Analysis](https://mlsanity.com/paper/2304.01219/) (2023)
- [Explainable Landscape-Aware Optimization Performance Prediction](https://mlsanity.com/paper/2110.11633/) (2021)
- [The Importance of Landscape Features for Performance Prediction of Modular CMA-ES Variants](https://mlsanity.com/paper/2204.07431/) (2022)
- [Assessing the Generalizability of a Performance Predictive Model](https://mlsanity.com/paper/2306.00040/) (2023)
- [Generalization Ability of Feature-based Performance Prediction Models: A Statistical Analysis across Benchmarks](https://mlsanity.com/paper/2405.12259/) (2024)
- [Explainable Landscape Analysis in Automated Algorithm Performance Prediction](https://mlsanity.com/paper/2203.11828/) (2022)
- [RF+clust for Leave-One-Problem-Out Performance Prediction](https://mlsanity.com/paper/2301.09524/) (2023)
- [A Survey of Features Used for Representing Black-box Single-objective Continuous Optimization](https://mlsanity.com/paper/2406.06629/) (2024)
- [The Pitfalls of Benchmarking in Algorithm Selection: What We Are Getting Wrong](https://mlsanity.com/paper/2505.07750/) (2025)
- [Automated Algorithm Selection for Radar Network Configuration](https://mlsanity.com/paper/2205.03670/) (2022)
- [Tools for Landscape Analysis of Optimisation Problems in Procedural Content Generation for Games](https://mlsanity.com/paper/2302.08479/) (2023)
- [Sensitivity Analysis of RF+clust for Leave-one-problem-out Performance Prediction](https://mlsanity.com/paper/2305.19375/) (2023)
- [On Constructing Algorithm Portfolios in Algorithm Selection for Computationally Expensive Black-box Optimization in the Fixed-budget Setting](https://mlsanity.com/paper/2405.10976/) (2024)
- [Does Dimensionality Reduction via Random Projections Preserve Landscape Features?](https://mlsanity.com/paper/2604.13230/) (2026)

## Abstract (arXiv)

Automated algorithm selection promises to support the user in the decisive task of selecting a most suitable algorithm for a given problem. A common component of these machine-trained techniques are regression models which predict the performance of a given algorithm on a previously unseen problem instance. In the context of numerical black-box optimization, such regression models typically build on exploratory landscape analysis (ELA), which quantifies several characteristics of the problem. These measures can be used to train a supervised performance regression model. First steps towards ELA-based performance regression have been made in the context of a fixed-target setting. In many applications, however, the user needs to select an algorithm that performs best within a given budget of function evaluations. Adopting this fixed-budget setting, we demonstrate that it is possible to achieve high-quality performance predictions with off-the-shelf supervised learning approaches, by suitably combining two differently trained regression models. We test this approach on a very challenging problem: algorithm selection on a portfolio of very similar algorithms, which we choose from the family of modular CMA-ES algorithms.