{"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/investigating-recurrent-neural-network-memory","title":"Investigating Recurrent Neural Network Memory Structures using Neuro-Evolution","arxiv_id":"1902.02390","date":"2019-02-06","proceeding":null,"authors":["Alexander Ororbia","Ahmed Ahmed Elsaid","Travis Desell"],"abstract":"This paper presents a new algorithm, Evolutionary eXploration of Augmenting\nMemory Models (EXAMM), which is capable of evolving recurrent neural networks\n(RNNs) using a wide variety of memory structures, such as Delta-RNN, GRU, LSTM,\nMGU and UGRNN cells. EXAMM evolved RNNs to perform prediction of large-scale,\nreal world time series data from the aviation and power industries. These data\nsets consist of very long time series (thousands of readings), each with a\nlarge number of potentially correlated and dependent parameters. Four different\nparameters were selected for prediction and EXAMM runs were performed using\neach memory cell type alone, each cell type with feed forward nodes, and with\nall possible memory cell types. Evolved RNN performance was measured using\nrepeated k-fold cross validation, resulting in 1210 EXAMM runs which evolved\n2,420,000 RNNs in 12,100 CPU hours on a high performance computing cluster.\nGeneralization of the evolved RNNs was examined statistically, providing\ninteresting findings that can help refine the RNN memory cell design as well as\ninform future neuro-evolution algorithms development.","url_abs":"http://arxiv.org/abs/1902.02390v2","url_pdf":"http://arxiv.org/pdf/1902.02390v2.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":"investigating-recurrent-neural-network-memory","repo_url":"https://github.com/travisdesell/exact","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":null,"task_name":"CPU"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[{"method_slug":"gru","method_name":"GRU"},{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}