{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:28:57Z","timestamp":1772555337565,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T00:00:00Z","timestamp":1748995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004837","name":"Spanish Ministry of Science, Innovation and Universities","doi-asserted-by":"publisher","award":["PID2023-147409NB-C21"],"award-info":[{"award-number":["PID2023-147409NB-C21"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>As early detection of voice disorders can significantly improve patients\u2019 situation, the automated detection using Artificial Intelligence techniques can be crucial in various applications in this scope. This paper introduces a multi-objective bio-inspired, AI-based optimization approach for the automated detection of voice disorders. Different multi-objective evolutionary algorithms (the Non-dominated Sorting Genetic Algorithm (NSGA-II), Strength Pareto Evolutionary Algorithm (SPEA-II), and the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA\/D)) have been compared to detect voice disorders by optimizing two conflicting objectives: error rate and the number of features. The optimization problem has been formulated as a wrapper-based algorithm for feature selection and multi-objective optimization relying on four machine learning algorithms: K-Nearest Neighbour algorithm (KNN), Random Forest (RF), Multilayer Perceptron (MLP), and Support Vector Machine (SVM). Three publicly available voice disorder datasets have been utilized, and results have been compared based on Inverted-Generational Distance, Hypervolume, spacing, and spread. The results reveal that NSGA-II with the MLP algorithm attained the best convergence and performance. Further, the conformal prediction is leveraged to quantify uncertainty in the feature-selected models, ensuring statistically valid confidence intervals for predictions.<\/jats:p>","DOI":"10.3390\/a18060338","type":"journal-article","created":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T03:57:34Z","timestamp":1749009454000},"page":"338","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Multi-Objective Bio-Inspired Optimization for Voice Disorders Detection: A Comparative Study"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9642-9597","authenticated-orcid":false,"given":"Maria","family":"Habib","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Automation and Robotics, School of Computer and Telecommunication Engineering, University of Granada, 18071 Granada, Spain"}]},{"given":"Victor","family":"Vicente-Palacios","sequence":"additional","affiliation":[{"name":"Department of Statistics, Faculty of Medicine, University of Salamanca, 37008 Salamanca, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4644-2894","authenticated-orcid":false,"given":"Pablo","family":"Garc\u00eda-S\u00e1nchez","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Automation and Robotics, School of Computer and Telecommunication Engineering, University of Granada, 18071 Granada, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,4]]},"reference":[{"key":"ref_1","unstructured":"Sapienza, C., and Ruddy, B. 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