Version 1
: Received: 30 October 2024 / Approved: 30 October 2024 / Online: 30 October 2024 (11:53:45 CET)
How to cite:
Dell'Aversana, P. Evolutionary Ensembles of Artificial Agents for Enhanced Mineralogical Analysis. Preprints2024, 2024102434. https://doi.org/10.20944/preprints202410.2434.v1
Dell'Aversana, P. Evolutionary Ensembles of Artificial Agents for Enhanced Mineralogical Analysis. Preprints 2024, 2024102434. https://doi.org/10.20944/preprints202410.2434.v1
Dell'Aversana, P. Evolutionary Ensembles of Artificial Agents for Enhanced Mineralogical Analysis. Preprints2024, 2024102434. https://doi.org/10.20944/preprints202410.2434.v1
APA Style
Dell'Aversana, P. (2024). Evolutionary Ensembles of Artificial Agents for Enhanced Mineralogical Analysis. Preprints. https://doi.org/10.20944/preprints202410.2434.v1
Chicago/Turabian Style
Dell'Aversana, P. 2024 "Evolutionary Ensembles of Artificial Agents for Enhanced Mineralogical Analysis" Preprints. https://doi.org/10.20944/preprints202410.2434.v1
Abstract
This paper presents a novel machine learning framework that applies evolutionary ensembles of artificial agents to mineralogical analysis and classification. The approach is based on hybridiza-tion techniques that combine diverse machine learning algorithms, creating large and effective communities of agents. These progressively mute and improve through crossover, hybridization, and selection, addressing the challenges of mineral recognition and classification from thin section images. By combining multiple machine learning techniques, the ensemble of agents autono-mously improves by evolving to adapt and enhance its ability to identify mineral species and classify different types of alterations. We detail the method, provide examples using synthetic and real data, and explore the potential to improve mineralogical analysis workflows through this dynamic, self-improving system.
Keywords
Deep Learning; Mineral,; Classification; Thin section; Machine Learning; Ensemble
Subject
Environmental and Earth Sciences, Geochemistry and Petrology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.