{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T16:46:08Z","timestamp":1783183568541,"version":"3.54.6"},"reference-count":40,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T00:00:00Z","timestamp":1707955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This study provides a comprehensive analysis of the combination of Genetic Algorithms (GA) and XGBoost, a well-known machine-learning model. The primary emphasis lies in hyperparameter optimization for fraud detection in smart grid applications. The empirical findings demonstrate a noteworthy enhancement in the model\u2019s performance metrics following optimization, particularly emphasizing a substantial increase in accuracy from 0.82 to 0.978. The precision, recall, and AUROC metrics demonstrate a clear improvement, indicating the effectiveness of optimizing the XGBoost model for fraud detection. The findings from our study significantly contribute to the expanding field of smart grid fraud detection. These results emphasize the potential uses of advanced metaheuristic algorithms to optimize complex machine-learning models. This work showcases significant progress in enhancing the accuracy and efficiency of fraud detection systems in smart grids.<\/jats:p>","DOI":"10.3390\/s24041230","type":"journal-article","created":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T03:57:29Z","timestamp":1707969449000},"page":"1230","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":74,"title":["Hyperparameter Optimization with Genetic Algorithms and XGBoost: A Step Forward in Smart Grid Fraud Detection"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6167-7141","authenticated-orcid":false,"given":"Adil","family":"Mehdary","sequence":"first","affiliation":[{"name":"LaGes, Hassania School of Public Works, Casablanca 20000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4193-6062","authenticated-orcid":false,"given":"Abdellah","family":"Chehri","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, Royal Military College of Canada, Kingston, ON K7K 7B4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdeslam","family":"Jakimi","sequence":"additional","affiliation":[{"name":"GL-ISI Team, Faculty of Science and Technology Errachidia, Moulay Ismail University, Meknes 50050, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0197-8313","authenticated-orcid":false,"given":"Rachid","family":"Saadane","sequence":"additional","affiliation":[{"name":"LaGes, Hassania School of Public Works, Casablanca 20000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2067","DOI":"10.1016\/S0301-4215(03)00182-4","article-title":"Electricity theft: A comparative analysis","volume":"32","author":"Smith","year":"2004","journal-title":"Energy Policy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"103563","DOI":"10.1016\/j.seta.2023.103563","article-title":"Personalized PV system recommendation for enhanced solar energy harvesting using deep learning and collaborative filtering","volume":"60","author":"Jbene","year":"2023","journal-title":"Sustainable Energy Technol. 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