| Issue |
EPJ Web Conf.
Volume 323, 2025
22nd International Metrology Congress (CIM2025)
|
|
|---|---|---|
| Article Number | 14001 | |
| Number of page(s) | 5 | |
| Section | Uncertainty & Risk Assessment | |
| DOI | https://doi.org/10.1051/epjconf/202532314001 | |
| Published online | 07 April 2025 | |
https://doi.org/10.1051/epjconf/202532314001
Case Study of Genetic Algorithms in Metrology: Assessment of Inter-laboratory Comparisons
Bureau International des Poids et Mesures, Pavillon de Breteuil, Sèvres, Cedex, F-92312, France
* Corresponding author: author@email.org
Published online: 7 April 2025
This study reviews conventional consensus estimation methods, including mean-based, median-based, and pooling-based approaches, and evaluates their performance under challenging scenarios involving outliers and deviations from normality. While traditional methods such as the weighted mean and weighted median often fail to handle extreme values and non-Gaussian distributions, advanced techniques like the Monte Carlo Median (MCM) and Power Moderated Mean (PMM) offer improved robustness. The Genetic Algorithm (GA), a novel optimization-based approach, demonstrates exceptional resilience to outliers. To facilitate its application, the GA is made available through the Python package consensusGen, accessible via the Python Package Index and GitHub. This ensures that practitioners and researchers can easily implement the GA in their consensus estimation tasks, benefiting from its superior robustness and precision.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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