# Comparing Algorithm Selection Approaches on Black-Box Optimization Problems

arXiv: https://arxiv.org/abs/2306.17585 · HTML: https://mlsanity.com/paper/2306.17585/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2023.

## Built on by (most cited first, 10 of 10)

- [LLaMoCo: Instruction Tuning of Large Language Models for Optimization Code Generation](https://mlsanity.com/paper/2403.01131/) (2024)
- [Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization](https://mlsanity.com/paper/2411.00625/) (2024)
- [A Survey of Features Used for Representing Black-box Single-objective Continuous Optimization](https://mlsanity.com/paper/2406.06629/) (2024)
- [GNNAS-Dock: Budget Aware Algorithm Selection with Graph Neural Networks for Molecular Docking](https://mlsanity.com/paper/2411.12597/) (2024)
- [Hilbert curves for efficient exploratory landscape analysis neighbourhood sampling](https://mlsanity.com/paper/2408.00526/) (2024)
- [Algorithm Selection with Probing Trajectories: Benchmarking the Choice of Classifier Model](https://mlsanity.com/paper/2501.11414/) (2025)
- [Customized Exploration of Landscape Features Driving Multi-Objective Combinatorial Optimization Performance](https://mlsanity.com/paper/2507.01638/) (2025)
- [Instance-Aware Algorithm Selection for Maximum Clique via a Dual-Channel Graph Neural Architecture](https://mlsanity.com/paper/2508.08005/) (2025)
- [On the Influence of the Feature Computation Budget on Per-Instance Algorithm Selection for Black-Box Optimization](https://mlsanity.com/paper/2605.04954/) (2026)
- [Unsupervised Multi-kernel Learning for Automated Algorithm Selection](https://mlsanity.com/paper/2607.19031/) (2026)

## Abstract (arXiv)

Performance complementarity of solvers available to tackle black-box optimization problems gives rise to the important task of algorithm selection (AS). Automated AS approaches can help replace tedious and labor-intensive manual selection, and have already shown promising performance in various optimization domains. Automated AS relies on machine learning (ML) techniques to recommend the best algorithm given the information about the problem instance. Unfortunately, there are no clear guidelines for choosing the most appropriate one from a variety of ML techniques. Tree-based models such as Random Forest or XGBoost have consistently demonstrated outstanding performance for automated AS. Transformers and other tabular deep learning models have also been increasingly applied in this context. We investigate in this work the impact of the choice of the ML technique on AS performance. We compare four ML models on the task of predicting the best solver for the BBOB problems for 7 different runtime budgets in 2 dimensions. While our results confirm that a per-instance AS has indeed impressive potential, we also show that the particular choice of the ML technique is of much minor importance.