# Trajectory-based Algorithm Selection with Warm-starting

arXiv: https://arxiv.org/abs/2204.06397 · HTML: https://mlsanity.com/paper/2204.06397/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2022.

## Built on by (most cited first, 8 of 8)

- [Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization](https://mlsanity.com/paper/2411.00625/) (2024)
- [On the Utility of Probing Trajectories for Algorithm-Selection](https://mlsanity.com/paper/2401.12745/) (2024)
- [A Survey of Features Used for Representing Black-box Single-objective Continuous Optimization](https://mlsanity.com/paper/2406.06629/) (2024)
- [Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis](https://mlsanity.com/paper/2211.09678/) (2022)
- [Identifying Easy Instances to Improve Efficiency of ML Pipelines for Algorithm-Selection](https://mlsanity.com/paper/2406.16999/) (2024)
- [Algorithm Selection with Probing Trajectories: Benchmarking the Choice of Classifier Model](https://mlsanity.com/paper/2501.11414/) (2025)
- [Automated Reproducibility Has a Problem Statement Problem](https://mlsanity.com/paper/2601.04226/) (2025)
- [Reinforcement learning to choose optimizers](https://mlsanity.com/paper/2609.01811/) (2026)

## Abstract (arXiv)

Landscape-aware algorithm selection approaches have so far mostly been relying on landscape feature extraction as a preprocessing step, independent of the execution of optimization algorithms in the portfolio. This introduces a significant overhead in computational cost for many practical applications, as features are extracted and computed via sampling and evaluating the problem instance at hand, similarly to what the optimization algorithm would perform anyway within its search trajectory. As suggested in Jankovic et al. (EvoAPPs 2021), trajectory-based algorithm selection circumvents the problem of costly feature extraction by computing landscape features from points that a solver sampled and evaluated during the optimization process. Features computed in this manner are used to train algorithm performance regression models, upon which a per-run algorithm selector is then built. In this work, we apply the trajectory-based approach onto a portfolio of five algorithms. We study the quality and accuracy of performance regression and algorithm selection models in the scenario of predicting different algorithm performances after a fixed budget of function evaluations. We rely on landscape features of the problem instance computed using one portion of the aforementioned budget of the same function evaluations. Moreover, we consider the possibility of switching between the solvers once, which requires them to be warm-started, i.e. when we switch, the second solver continues the optimization process already being initialized appropriately by making use of the information collected by the first solver. In this new context, we show promising performance of the trajectory-based per-run algorithm selection with warm-starting.