# Analyzing the Impact of Undersampling on the Benchmarking and Configuration of Evolutionary Algorithms

arXiv: https://arxiv.org/abs/2204.09353 · HTML: https://mlsanity.com/paper/2204.09353/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2022.

## Built on by (most cited first, 4 of 4)

- [Automated Design of Metaheuristic Algorithms: A Survey](https://mlsanity.com/paper/2303.06532/) (2023)
- [To Switch or not to Switch: Predicting the Benefit of Switching between Algorithms based on Trajectory Features](https://mlsanity.com/paper/2302.09075/) (2023)
- [Pareto-Optimal Anytime Algorithms via Bayesian Racing](https://mlsanity.com/paper/2603.08493/) (2026)
- [Learning to Assess the Reliability of Number-of-Runs Estimation in Stochastic Optimization](https://mlsanity.com/paper/2605.28309/) (2026)

## Abstract (arXiv)

The stochastic nature of iterative optimization heuristics leads to inherently noisy performance measurements. Since these measurements are often gathered once and then used repeatedly, the number of collected samples will have a significant impact on the reliability of algorithm comparisons. We show that care should be taken when making decisions based on limited data. Particularly, we show that the number of runs used in many benchmarking studies, e.g., the default value of 15 suggested by the COCO environment, can be insufficient to reliably rank algorithms on well-known numerical optimization benchmarks. Additionally, methods for automated algorithm configuration are sensitive to insufficient sample sizes. This may result in the configurator choosing a `lucky' but poor-performing configuration despite exploring better ones. We show that relying on mean performance values, as many configurators do, can require a large number of runs to provide accurate comparisons between the considered configurations. Common statistical tests can greatly improve the situation in most cases but not always. We show examples of performance losses of more than 20%, even when using statistical races to dynamically adjust the number of runs, as done by irace. Our results underline the importance of appropriately considering the statistical distribution of performance values.