{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/evolving-deep-neural-networks","title":"Evolving Deep Neural Networks","arxiv_id":"1703.00548","date":"2017-03-01","proceeding":null,"authors":["Risto Miikkulainen","Jason Liang","Elliot Meyerson","Aditya Rawal","Dan Fink","Olivier Francon","Bala Raju","Hormoz Shahrzad","Arshak Navruzyan","Nigel Duffy","Babak Hodjat"],"abstract":"The success of deep learning depends on finding an architecture to fit the\ntask. As deep learning has scaled up to more challenging tasks, the\narchitectures have become difficult to design by hand. This paper proposes an\nautomated method, CoDeepNEAT, for optimizing deep learning architectures\nthrough evolution. By extending existing neuroevolution methods to topology,\ncomponents, and hyperparameters, this method achieves results comparable to\nbest human designs in standard benchmarks in object recognition and language\nmodeling. It also supports building a real-world application of automated image\ncaptioning on a magazine website. Given the anticipated increases in available\ncomputing power, evolution of deep networks is promising approach to\nconstructing deep learning applications in the future.","url_abs":"http://arxiv.org/abs/1703.00548v2","url_pdf":"http://arxiv.org/pdf/1703.00548v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"evolving-deep-neural-networks","repo_url":"https://github.com/ddpalacios/Evolving-Architecture","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"evolving-deep-neural-networks","repo_url":"https://github.com/j9650/CoDeepNEAT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"evolving-deep-neural-networks","repo_url":"https://github.com/sbcblab/Keras-CoDeepNEAT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"evolving-deep-neural-networks","repo_url":"https://github.com/shrutimohanty/Hcodeep","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"image-captioning","task_name":"Image Captioning"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"object-recognition","task_name":"Object Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.00548","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}