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However, the performance of deep learning algorithms is often highly dependent on the design of their neural network architectures; the process of manually designing neural network architectures is usually very time-consuming and resource-intensive. In addition, different types of networks have distinct advantages, and how to automatically hybridize these networks to design an effective network for seizure prediction remains a substantial challenge. To address this issue, this paper proposes a hybrid convolutional, Mamba, and spiking neural network architecture search framework (CMS-NAS) based on a multiobjective evolutionary algorithm for seizure prediction, which can automatically design a lightweight network capable of comprehensively extracting electroencephalogram features. By simulating the natural selection mechanism, CMS-NAS iteratively evolves candidate networks through populations and uses a multiobjective scoring function to simultaneously optimize the model\u2019s prediction accuracy and model size. Extensive experiments are conducted on 2 public datasets and a private dataset, and the experimental results show that the network model searched by the CMS-NAS algorithm achieves competitive performance in seizure prediction.<\/jats:p>","DOI":"10.34133\/cbsystems.0689","type":"journal-article","created":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T10:44:40Z","timestamp":1787741080000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark_01","source":"Crossref","is-referenced-by-count":0,"title":["A Hybrid Convolutional, Mamba and Spiking Neural Network Architecture Search for Seizure Prediction"],"prefix":"10.34133","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-1571-716X","authenticated-orcid":false,"given":"Bo","family":"Yu","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, \rHefei University of Technology, Hefei 230009, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0195-1003","authenticated-orcid":true,"given":"Chang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, \rHefei University of Technology, Hefei 230009, China."},{"name":"Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, \rHefei University of Technology, Hefei 230009, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rencheng","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, \rHefei University of Technology, Hefei 230009, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0109-4611","authenticated-orcid":false,"given":"Xiang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, \rUniversity of Science and Technology of China, Hefei 230001, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruobing","family":"Qian","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, \rUniversity of Science and Technology of China, Hefei 230001, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xun","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, \rUniversity of Science and Technology of China, Hefei 230001, China."},{"name":"Department of Electronic Engineering and Information Science, \rUniversity of Science and Technology of China, Hefei 230026, China."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","published-online":{"date-parts":[[2026,9,30]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.2174\/0929867330666230117160632"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2013.02.014"},{"key":"e_1_3_3_4_2","doi-asserted-by":"crossref","unstructured":"Ahmad M Saeed M Saleem S Kamboh AM. 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