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WANNs offer the potential for lighter networks with shared weights, utilizing a two-layer cooperative framework to optimize network architecture and shared weights. The proposed metaheuristic is tested on real-world crop datasets and benchmarked against state-of-the-art algorithms using standard regression metrics. While not claiming WANN as the definitive solution, the model demonstrates significant potential in crop forecasting with lightweight architectures. The optimized WANN models achieve a mean absolute error (MAE) of 0.017698 and an <jats:italic>R<\/jats:italic>-squared (<jats:inline-formula><jats:alternatives><jats:tex-math>$$R^2$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msup>\n                    <mml:mi>R<\/mml:mi>\n                    <mml:mn>2<\/mml:mn>\n                  <\/mml:msup>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>) score of 0.886555, indicating promising forecasting performance. Statistical analysis and Simulator for Autonomy and Generality Evaluation (SAGE) validate the improvement significance and feature importance of the proposed approach.<\/jats:p>","DOI":"10.1007\/s00521-024-09850-4","type":"journal-article","created":{"date-parts":[[2024,5,10]],"date-time":"2024-05-10T07:02:09Z","timestamp":1715324529000},"page":"14727-14756","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Evaluating the performance of metaheuristic-tuned weight agnostic neural networks for crop yield prediction"],"prefix":"10.1007","volume":"36","author":[{"given":"Luka","family":"Jovanovic","sequence":"first","affiliation":[]},{"given":"Miodrag","family":"Zivkovic","sequence":"additional","affiliation":[]},{"given":"Nebojsa","family":"Bacanin","sequence":"additional","affiliation":[]},{"given":"Milos","family":"Dobrojevic","sequence":"additional","affiliation":[]},{"given":"Vladimir","family":"Simic","sequence":"additional","affiliation":[]},{"given":"Kishor Kumar","family":"Sadasivuni","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1664-9210","authenticated-orcid":false,"given":"Erfan Babaee","family":"Tirkolaee","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,10]]},"reference":[{"key":"9850_CR1","doi-asserted-by":"publisher","first-page":"116158","DOI":"10.1016\/j.eswa.2021.116158","volume":"191","author":"L Abualigah","year":"2022","unstructured":"Abualigah L, Abd Elaziz M, Sumari P, Geem ZW, Gandomi AH (2022) Reptile search algorithm (rsa): a nature-inspired meta-heuristic optimizer. 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