{"id":"https://openalex.org/W2346006176","doi":"https://doi.org/10.1145/2908812.2908941","title":"Evolving Deep LSTM-based Memory Networks using an Information Maximization Objective","display_name":"Evolving Deep LSTM-based Memory Networks using an Information Maximization Objective","publication_year":2016,"publication_date":"2016-07-20","ids":{"openalex":"https://openalex.org/W2346006176","doi":"https://doi.org/10.1145/2908812.2908941","mag":"2346006176"},"language":"en","primary_location":{"id":"doi:10.1145/2908812.2908941","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2908812.2908941","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Genetic and Evolutionary Computation Conference 2016","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111881609","display_name":"Aditya Rawal","orcid":null},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aditya Rawal","raw_affiliation_strings":["University of Texas at Austin, Austin, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Texas at Austin, Austin, TX, USA","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020441009","display_name":"Risto Miikkulainen","orcid":"https://orcid.org/0000-0002-0062-0037"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Risto Miikkulainen","raw_affiliation_strings":["University of Texas at Austin, Austin, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Texas at Austin, Austin, TX, USA","institution_ids":["https://openalex.org/I86519309"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I86519309"],"apc_list":null,"apc_paid":null,"fwci":3.4918,"has_fulltext":false,"cited_by_count":43,"citation_normalized_percentile":{"value":0.94684552,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"501","last_page":"508"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9886999726295471,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8587157726287842},{"id":"https://openalex.org/keywords/neuroevolution","display_name":"Neuroevolution","score":0.6947817802429199},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6854637861251831},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5936437845230103},{"id":"https://openalex.org/keywords/maximization","display_name":"Maximization","score":0.5861862897872925},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5449217557907104},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5423780679702759},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5278969407081604},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4225981831550598},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3594961166381836},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.08386656641960144}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8587157726287842},{"id":"https://openalex.org/C118070581","wikidata":"https://www.wikidata.org/wiki/Q2060528","display_name":"Neuroevolution","level":3,"score":0.6947817802429199},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6854637861251831},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5936437845230103},{"id":"https://openalex.org/C2776330181","wikidata":"https://www.wikidata.org/wiki/Q18358244","display_name":"Maximization","level":2,"score":0.5861862897872925},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5449217557907104},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5423780679702759},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5278969407081604},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4225981831550598},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3594961166381836},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.08386656641960144},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2908812.2908941","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2908812.2908941","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Genetic and Evolutionary Computation Conference 2016","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3127719495","display_name":"The Role of Emotion and Communication in Cooperative Behavior","funder_award_id":"5r01gm105042-03","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W139960808","https://openalex.org/W1525783482","https://openalex.org/W1654657067","https://openalex.org/W1899504021","https://openalex.org/W1945976014","https://openalex.org/W1971127389","https://openalex.org/W2034806191","https://openalex.org/W2048570735","https://openalex.org/W2064675550","https://openalex.org/W2079246448","https://openalex.org/W2081036512","https://openalex.org/W2084494117","https://openalex.org/W2095786862","https://openalex.org/W2096533821","https://openalex.org/W2099052794","https://openalex.org/W2100097207","https://openalex.org/W2102113734","https://openalex.org/W2103581399","https://openalex.org/W2108384452","https://openalex.org/W2111935653","https://openalex.org/W2118253315","https://openalex.org/W2120181489","https://openalex.org/W2123372395","https://openalex.org/W2123663688","https://openalex.org/W2124817648","https://openalex.org/W2133564696","https://openalex.org/W2140826469","https://openalex.org/W2165776394","https://openalex.org/W2169803171","https://openalex.org/W2938753230","https://openalex.org/W2964308564","https://openalex.org/W2999905431","https://openalex.org/W3125358592","https://openalex.org/W4246329541","https://openalex.org/W6674304311","https://openalex.org/W7043000401"],"related_works":["https://openalex.org/W2168909409","https://openalex.org/W2144357723","https://openalex.org/W2950402165","https://openalex.org/W2114981325","https://openalex.org/W4302038648","https://openalex.org/W2099397840","https://openalex.org/W2249125133","https://openalex.org/W2811365478","https://openalex.org/W2735321217","https://openalex.org/W2962258836"],"abstract_inverted_index":{"Reinforcement":[0],"Learning":[1],"agents":[2],"with":[3,23],"memory":[4,21,33,48,107,134,143],"are":[5,109,123],"constructed":[6],"in":[7,88],"this":[8],"paper":[9],"by":[10,36,111,125],"extending":[11],"neuroevolutionary":[12],"algorithm":[13],"NEAT":[14],"to":[15,46,68],"incorporate":[16],"LSTM":[17,45,90],"cells,":[18],"i.e.":[19],"special":[20],"units":[22],"gating":[24],"logic.":[25],"Initial":[26],"evaluation":[27],"on":[28,131],"POMDP":[29],"tasks":[30,135],"indicated":[31],"that":[32,82,137,145],"solutions":[34],"obtained":[35],"evolving":[37],"LSTMs":[38],"outperform":[39,146],"traditional":[40,147],"RNNs.":[41,148],"Scaling":[42],"neuroevolution":[43,138],"of":[44,64],"deep":[47],"problems":[49],"is":[50,57,80,95],"challenging":[51],"because:":[52],"(1)":[53],"the":[54,84,89,101,114,118,121,127],"fitness":[55],"landscape":[56],"deceptive,":[58],"and":[59],"(2)":[60],"a":[61,75],"large":[62],"number":[63],"associated":[65],"parameters":[66],"need":[67],"be":[69],"optimized.":[70],"To":[71],"overcome":[72],"these":[73],"challenges,":[74],"new":[76],"secondary":[77],"optimization":[78],"objective":[79],"introduced":[81],"maximizes":[83],"information":[85],"(Info-max)":[86],"stored":[87],"network.":[91],"The":[92],"network":[93],"training":[94],"split":[96],"into":[97],"two":[98,132],"phases.":[99],"In":[100,117],"first":[102],"phase":[103],"(unsupervised":[104],"phase),":[105],"independent":[106],"modules":[108],"evolved":[110],"optimizing":[112,126],"for":[113],"info-max":[115],"objective.":[116],"second":[119],"phase,":[120],"networks":[122],"trained":[124],"task":[128],"fitness.":[129],"Results":[130],"different":[133],"indicate":[136],"can":[139],"discover":[140],"powerful":[141],"LSTM-based":[142],"solution":[144]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":10},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":1}],"updated_date":"2026-08-06T08:24:18.245995","created_date":"2025-10-10T00:00:00"}
