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Traditional linear models perform well in handling simple traits but have limitations in extracting nonlinear features for complex traits. The introduction of deep learning (DL) techniques has provided a new approach to GP, especially suited for high-dimensional data processing and complex trait prediction. However, traditional DL models require manual design of the network architecture, which necessitates continuous experimentation and modification. In this paper, we propose a new framework, MDNN, that utilizes the memetic algorithm for neural architecture search and automatically optimizes the network architecture. Compared with the DNNGP, MDNN achieved a 36.49% improvement in the average Pearson correlation coefficient on the wheat599 dataset and a 12.28% improvement on the wheat2000 dataset.<\/jats:p>","DOI":"10.1093\/bib\/bbaf352","type":"journal-article","created":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T07:19:19Z","timestamp":1751872759000},"source":"Crossref","is-referenced-by-count":0,"title":["MDNN: memetic deep neural network for genomic prediction"],"prefix":"10.1093","volume":"26","author":[{"given":"Yijun","family":"Mao","sequence":"first","affiliation":[{"name":"College of Mathematics and Informatics, South China Agricultural University , 483 Wushan Road, Tianhe District, Guangzhou 510642 ,","place":["China"]},{"name":"National Key Laboratory of Data Space Technology and System , 3 Minzhuang Road, Haidian District, Beijing 100195 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