# To Switch or not to Switch: Predicting the Benefit of Switching between Algorithms based on Trajectory Features

arXiv: https://arxiv.org/abs/2302.09075 · HTML: https://mlsanity.com/paper/2302.09075/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 17 Feb 2023.

## Builds on (most cited first, 16 of 16)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting](https://mlsanity.com/paper/1603.08785/) (2016)
- [Automated Algorithm Selection: Survey and Perspectives](https://mlsanity.com/paper/1811.11597/) (2018)
- [Automated Dynamic Algorithm Configuration](https://mlsanity.com/paper/2205.13881/) (2022)
- [DACBench: A Benchmark Library for Dynamic Algorithm Configuration](https://mlsanity.com/paper/2105.08541/) (2021)
- [IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics](https://mlsanity.com/paper/2111.04077/) (2021)
- [IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics](https://mlsanity.com/paper/2007.03953/) (2020)
- [Tuning as a Means of Assessing the Benefits of New Ideas in Interplay with Existing Algorithmic Modules](https://mlsanity.com/paper/2102.12905/) (2021)
- [Per-run Algorithm Selection with Warm-starting using Trajectory-based Features](https://mlsanity.com/paper/2204.09483/) (2022)
- [Towards Feature-Based Performance Regression Using Trajectory Data](https://mlsanity.com/paper/2102.05370/) (2021)
- [Black-Box Optimization Revisited: Improving Algorithm Selection Wizards through Massive Benchmarking](https://mlsanity.com/paper/2010.04542/) (2020)
- [Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions](https://mlsanity.com/paper/2102.00736/) (2021)
- [Online Selection of CMA-ES Variants](https://mlsanity.com/paper/1904.07801/) (2019)
- [Towards Dynamic Algorithm Selection for Numerical Black-Box Optimization: Investigating BBOB as a Use Case](https://mlsanity.com/paper/2006.06586/) (2020)
- [Analyzing the Impact of Undersampling on the Benchmarking and Configuration of Evolutionary Algorithms](https://mlsanity.com/paper/2204.09353/) (2022)

## Built on by (most cited first, 9 of 9)

- [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)
- [Improving Algorithm-Selection and Performance-Prediction via Learning Discriminating Training Samples](https://mlsanity.com/paper/2404.05359/) (2024)
- [Identifying Easy Instances to Improve Efficiency of ML Pipelines for Algorithm-Selection](https://mlsanity.com/paper/2406.16999/) (2024)
- [When Switching Algorithms Helps: A Theoretical Study of Online Algorithm Selection](https://mlsanity.com/paper/2604.07473/) (2026)
- [Algorithm Selection with Probing Trajectories: Benchmarking the Choice of Classifier Model](https://mlsanity.com/paper/2501.11414/) (2025)
- [Greedy Restart Schedules: A Baseline for Dynamic Algorithm Selection on Numerical Black-box Optimization Problems](https://mlsanity.com/paper/2504.11440/) (2025)
- [A Review on Single-Problem Multi-Attempt Heuristic Optimization](https://mlsanity.com/paper/2509.26321/) (2025)
- [Reinforcement learning to choose optimizers](https://mlsanity.com/paper/2609.01811/) (2026)

## Abstract (arXiv)

Dynamic algorithm selection aims to exploit the complementarity of multiple optimization algorithms by switching between them during the search. While these kinds of dynamic algorithms have been shown to have potential to outperform their component algorithms, it is still unclear how this potential can best be realized. One promising approach is to make use of landscape features to enable a per-run trajectory-based switch. Here, the samples seen by the first algorithm are used to create a set of features which describe the landscape from the perspective of the algorithm. These features are then used to predict what algorithm to switch to. In this work, we extend this per-run trajectory-based approach to consider a wide variety of potential points at which to perform the switch. We show that using a sliding window to capture the local landscape features contains information which can be used to predict whether a switch at that point would be beneficial to future performance. By analyzing the resulting models, we identify what features are most important to these predictions. Finally, by evaluating the importance of features and comparing these values between multiple algorithms, we show clear differences in the way the second algorithm interacts with the local landscape features found before the switch.