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

TL;DR

This paper investigates how undersampling affects the reliability of benchmarking and configuration of evolutionary algorithms, highlighting the need for sufficient sample sizes and proper statistical methods to ensure accurate comparisons.

Contribution

It demonstrates that common benchmarking practices and automated configuration methods are often underpowered due to insufficient sample sizes, and emphasizes the importance of statistical considerations.

Findings

15 runs may be insufficient for reliable algorithm ranking

Performance losses of over 20% can occur with inadequate sampling

Statistical tests improve comparison reliability but are not foolproof

Abstract

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.