# Landscape Features in Single-Objective Continuous Optimization: Have We Hit a Wall in Algorithm Selection Generalization?

arXiv: https://arxiv.org/abs/2501.17663 · HTML: https://mlsanity.com/paper/2501.17663/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 29 Jan 2025.

## Builds on (most cited first, 9 of 9)

- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [Automated Algorithm Selection: Survey and Perspectives](https://mlsanity.com/paper/1811.11597/) (2018)
- [DoE2Vec: Deep-learning Based Features for Exploratory Landscape Analysis](https://mlsanity.com/paper/2304.01219/) (2023)
- [DynamoRep: Trajectory-Based Population Dynamics for Classification of Black-box Optimization Problems](https://mlsanity.com/paper/2306.05438/) (2023)
- [On the Utility of Probing Trajectories for Algorithm-Selection](https://mlsanity.com/paper/2401.12745/) (2024)
- [Benchmarking Feature-based Algorithm Selection Systems for Black-box Numerical Optimization](https://mlsanity.com/paper/2109.08377/) (2021)
- [Using Affine Combinations of BBOB Problems for Performance Assessment](https://mlsanity.com/paper/2303.04573/) (2023)
- [Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization](https://mlsanity.com/paper/2404.07539/) (2024)
- [TransOpt: Transformer-based Representation Learning for Optimization Problem Classification](https://mlsanity.com/paper/2311.18035/) (2023)

## Built on by (most cited first, 7 of 7)

- [The Pitfalls of Benchmarking in Algorithm Selection: What We Are Getting Wrong](https://mlsanity.com/paper/2505.07750/) (2025)
- [LLM Driven Design of Continuous Optimization Problems with Controllable High-level Properties](https://mlsanity.com/paper/2601.18846/) (2026)
- [Molecular Embedding-Based Algorithm Selection in Protein-Ligand Docking](https://mlsanity.com/paper/2512.02328/) (2025)
- [Geometric Probing for Algorithm Selection in Continuous Black-Box Optimization](https://mlsanity.com/paper/2604.09095/) (2026)
- [On the Influence of the Feature Computation Budget on Per-Instance Algorithm Selection for Black-Box Optimization](https://mlsanity.com/paper/2605.04954/) (2026)
- [Evaluating Real-World Generalizability of Algorithm Selection Models](https://mlsanity.com/paper/2606.02016/) (2026)
- [Unsupervised Multi-kernel Learning for Automated Algorithm Selection](https://mlsanity.com/paper/2607.19031/) (2026)

## Abstract (arXiv)

%% Text of abstract The process of identifying the most suitable optimization algorithm for a specific problem, referred to as algorithm selection (AS), entails training models that leverage problem landscape features to forecast algorithm performance. A significant challenge in this domain is ensuring that AS models can generalize effectively to novel, unseen problems. This study evaluates the generalizability of AS models based on different problem representations in the context of single-objective continuous optimization. In particular, it considers the most widely used Exploratory Landscape Analysis features, as well as recently proposed Topological Landscape Analysis features, and features based on deep learning, such as DeepELA, TransOptAS and Doe2Vec. Our results indicate that when presented with out-of-distribution evaluation data, none of the feature-based AS models outperform a simple baseline model, i.e., a Single Best Solver.