Case Study of Genetic Algorithms in Metrology: Assessment of Inter-laboratory Comparisons

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 weight...

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Main Author: Coulon Romain
Format: Article
Language:English
Published: EDP Sciences 2025-01-01
Series:EPJ Web of Conferences
Online Access:https://www.epj-conferences.org/articles/epjconf/pdf/2025/08/epjconf_cim2025_14001.pdf
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author Coulon Romain
author_facet Coulon Romain
author_sort Coulon Romain
collection DOAJ
description 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.
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spelling doaj-art-25db73e77df84440b5d0cf18bfe9480e2025-08-20T03:06:30ZengEDP SciencesEPJ Web of Conferences2100-014X2025-01-013231400110.1051/epjconf/202532314001epjconf_cim2025_14001Case Study of Genetic Algorithms in Metrology: Assessment of Inter-laboratory ComparisonsCoulon Romain0Bureau International des Poids et Mesures, Pavillon de BreteuilThis 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.https://www.epj-conferences.org/articles/epjconf/pdf/2025/08/epjconf_cim2025_14001.pdf
spellingShingle Coulon Romain
Case Study of Genetic Algorithms in Metrology: Assessment of Inter-laboratory Comparisons
EPJ Web of Conferences
title Case Study of Genetic Algorithms in Metrology: Assessment of Inter-laboratory Comparisons
title_full Case Study of Genetic Algorithms in Metrology: Assessment of Inter-laboratory Comparisons
title_fullStr Case Study of Genetic Algorithms in Metrology: Assessment of Inter-laboratory Comparisons
title_full_unstemmed Case Study of Genetic Algorithms in Metrology: Assessment of Inter-laboratory Comparisons
title_short Case Study of Genetic Algorithms in Metrology: Assessment of Inter-laboratory Comparisons
title_sort case study of genetic algorithms in metrology assessment of inter laboratory comparisons
url https://www.epj-conferences.org/articles/epjconf/pdf/2025/08/epjconf_cim2025_14001.pdf
work_keys_str_mv AT coulonromain casestudyofgeneticalgorithmsinmetrologyassessmentofinterlaboratorycomparisons