{"id":"https://openalex.org/W1996862194","doi":"https://doi.org/10.1145/1388969.1388986","title":"A study for multi-objective fitness function for time series forecasting with intelligent techniques","display_name":"A study for multi-objective fitness function for time series forecasting with intelligent techniques","publication_year":2008,"publication_date":"2008-07-12","ids":{"openalex":"https://openalex.org/W1996862194","doi":"https://doi.org/10.1145/1388969.1388986","mag":"1996862194"},"language":"en","primary_location":{"id":"doi:10.1145/1388969.1388986","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1388969.1388986","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th annual conference companion on Genetic and evolutionary computation","raw_type":"proceedings-article"},"type":"article","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/A5051152681","display_name":"Aranildo R. Lima","orcid":"https://orcid.org/0000-0003-2514-891X"},"institutions":[{"id":"https://openalex.org/I25112270","display_name":"Universidade Federal de Pernambuco","ror":"https://ror.org/047908t24","country_code":"BR","type":"education","lineage":["https://openalex.org/I25112270"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Aranildo Rodrigues Lima Junior","raw_affiliation_strings":["Federal University of Pernambuco, Recife, Brazil"],"affiliations":[{"raw_affiliation_string":"Federal University of Pernambuco, Recife, Brazil","institution_ids":["https://openalex.org/I25112270"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5051152681"],"corresponding_institution_ids":["https://openalex.org/I25112270"],"apc_list":null,"apc_paid":null,"fwci":1.1441,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.83368952,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"57","issue":null,"first_page":"1843","last_page":"1846"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9990000128746033,"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/T10320","display_name":"Neural Networks and Applications","score":0.9990000128746033,"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":0.998199999332428,"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/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9944999814033508,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/fitness-function","display_name":"Fitness function","score":0.8341161012649536},{"id":"https://openalex.org/keywords/grasp","display_name":"GRASP","score":0.7633483409881592},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6393376588821411},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6051805019378662},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5750998854637146},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4711521863937378},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.46623751521110535},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4585757851600647},{"id":"https://openalex.org/keywords/evolutionary-algorithm","display_name":"Evolutionary algorithm","score":0.4439869523048401},{"id":"https://openalex.org/keywords/fitness-approximation","display_name":"Fitness approximation","score":0.4379502534866333},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40031546354293823},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.38041093945503235},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3554261326789856},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2089134156703949}],"concepts":[{"id":"https://openalex.org/C176066374","wikidata":"https://www.wikidata.org/wiki/Q629118","display_name":"Fitness function","level":3,"score":0.8341161012649536},{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.7633483409881592},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6393376588821411},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6051805019378662},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5750998854637146},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4711521863937378},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.46623751521110535},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4585757851600647},{"id":"https://openalex.org/C159149176","wikidata":"https://www.wikidata.org/wiki/Q14489129","display_name":"Evolutionary algorithm","level":2,"score":0.4439869523048401},{"id":"https://openalex.org/C148392497","wikidata":"https://www.wikidata.org/wiki/Q16250539","display_name":"Fitness approximation","level":4,"score":0.4379502534866333},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40031546354293823},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.38041093945503235},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3554261326789856},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2089134156703949},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1388969.1388986","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1388969.1388986","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th annual conference companion on Genetic and evolutionary computation","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1496940124","https://openalex.org/W1512383952","https://openalex.org/W1549386224","https://openalex.org/W1576818901","https://openalex.org/W2008151606","https://openalex.org/W2047175362","https://openalex.org/W2103496339","https://openalex.org/W2131141229","https://openalex.org/W2607589993","https://openalex.org/W2997776631","https://openalex.org/W4291439952","https://openalex.org/W6629902689","https://openalex.org/W6672895715"],"related_works":["https://openalex.org/W1622672652","https://openalex.org/W2017545286","https://openalex.org/W2159937791","https://openalex.org/W1995719995","https://openalex.org/W2164084664","https://openalex.org/W4299643771","https://openalex.org/W2951823782","https://openalex.org/W2036107212","https://openalex.org/W1606502848","https://openalex.org/W2550292406"],"abstract_inverted_index":{"This":[0,82],"paper":[1],"presents":[2],"an":[3,37],"study":[4],"of":[5,31,36,73,88],"a":[6,96],"Hybrid":[7],"method":[8],"for":[9,29],"time":[10,47],"series":[11,48],"prediction,":[12],"called":[13],"GRASPES,":[14],"based":[15,78],"on":[16],"Greedy":[17],"Randomized":[18],"Adaptive":[19],"Search":[20],"Procedure":[21],"(GRASP)":[22],"Algorithm":[23],"and":[24,34,51,57],"Evolutionary":[25],"Strategies":[26],"(ES)":[27],"concepts":[28],"tuning":[30],"the":[32,52,64,89],"structure":[33],"parameters":[35],"Artificial":[38],"Neural":[39],"Network":[40],"(ANN).":[41],"An":[42],"experimental":[43],"investigation":[44],"with":[45],"two":[46],"is":[49],"conducted":[50],"results":[53,83],"achieved":[54],"are":[55,70],"discussed":[56],"compared":[58],"to":[59,95],"other":[60],"works":[61],"reported":[62],"in":[63],"literature.":[65],"Distinct":[66],"fitness":[67,79,90],"functions":[68,80],"evaluations":[69],"shown,":[71],"instead":[72],"conventional":[74],"MSE":[75],"or":[76],"NMSE":[77],"evaluation.":[81],"shown":[84],"that":[85],"small":[86],"changes":[87],"function":[91],"evaluation":[92],"could":[93],"lead":[94],"significantly":[97],"improved":[98],"performance.":[99]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}