# The Impact of Hyper-Parameter Tuning for Landscape-Aware Performance Regression and Algorithm Selection

arXiv: https://arxiv.org/abs/2104.09272 · HTML: https://mlsanity.com/paper/2104.09272/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2021.

## Built on by (most cited first, 11 of 11)

- [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)
- [PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization](https://mlsanity.com/paper/2310.10685/) (2023)
- [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)
- [Automated Algorithm Selection for Radar Network Configuration](https://mlsanity.com/paper/2205.03670/) (2022)
- [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)
- [Reinforcement learning to choose optimizers](https://mlsanity.com/paper/2609.01811/) (2026)

## Abstract (arXiv)

Automated algorithm selection and configuration methods that build on exploratory landscape analysis (ELA) are becoming very popular in Evolutionary Computation. However, despite a significantly growing number of applications, the underlying machine learning models are often chosen in an ad-hoc manner. We show in this work that three classical regression methods are able to achieve meaningful results for ELA-based algorithm selection. For those three models -- random forests, decision trees, and bagging decision trees -- the quality of the regression models is highly impacted by the chosen hyper-parameters. This has significant effects also on the quality of the algorithm selectors that are built on top of these regressions. By comparing a total number of 30 different models, each coupled with 2 complementary regression strategies, we derive guidelines for the tuning of the regression models and provide general recommendations for a more systematic use of classical machine learning models in landscape-aware algorithm selection. We point out that a choice of the machine learning model merits to be carefully undertaken and further investigated.