{"updated":"2025-01-22T15:42:21.033016+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00033420","sets":["1164:2735:2772:2774"]},"path":["2774"],"owner":"1","recid":"33420","title":["出生前淘汰による遺伝的アルゴリズムの効率化"],"pubdate":{"attribute_name":"公開日","attribute_value":"2002-09-20"},"_buckets":{"deposit":"89c23ebc-4999-4379-9189-f8e6a9578287"},"_deposit":{"id":"33420","pid":{"type":"depid","value":"33420","revision_id":0},"owners":[1],"status":"published","created_by":1},"item_title":"出生前淘汰による遺伝的アルゴリズムの効率化","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"出生前淘汰による遺伝的アルゴリズムの効率化"},{"subitem_title":"An Efficient Genetic Algorithm using Prenatal Selection","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2002-09-20","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"名古屋工業大学"},{"subitem_text_value":"名古屋工業大学"},{"subitem_text_value":"名古屋工業大学"},{"subitem_text_value":"名古屋工業大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Nagoya Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Nagoya Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Nagoya Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Nagoya Institute of Technology","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/33420/files/IPSJ-MPS02041004.pdf"},"date":[{"dateType":"Available","dateValue":"2004-09-20"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MPS02041004.pdf","filesize":[{"value":"819.4 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"17"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"b81114ed-bec0-4150-82bb-22ed83b62243","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2002 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"武藤, 敦子"},{"creatorName":"中村, 剛士"},{"creatorName":"加藤昇平"},{"creatorName":"伊藤, 英則"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Atsuko, Mutoh","creatorNameLang":"en"},{"creatorName":"Tsuyoshi, Nakamura","creatorNameLang":"en"},{"creatorName":"Shohei, Kato","creatorNameLang":"en"},{"creatorName":"Hidenori, Itoh","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10505667","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"遺伝的アルゴリズムは組合せ最適化問題を解くための有効な手法の１つである．しかし問題によっては適応度計算に多大な時間がかかるものがある．今までに提案されている世代交代モデルの中のいくつかには，「子との競争に勝った親のみが生存を許される戦略」を採用しているものがあり，ある程度の効果をあげているが，探索を進めるにつれ個体適応度の改善される割合が少なくなり計算時間に見合う適応度が獲得できないという問題点がある．そこで本稿では， ニューラルネットワークを用いた出生前診断を導入することで，適応度計算時間の短縮を試みる手法を提案する．さらにこれを実問題へ適用し，提案手法の有効性を示す．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Genetic algorithm is an effective method to solve combinatorial optimization problems, on the other hand it required a lot of execution time to calculating fitness. This paper proposes a novel approach to acquire the high-fitness individuals as fast as possible by prenatal diagnosis using neural network. In the experiments the proposed method had higher fitness than the conventional method.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"16","bibliographic_titles":[{"bibliographic_title":"情報処理学会研究報告数理モデル化と問題解決（MPS）"}],"bibliographicPageStart":"13","bibliographicIssueDates":{"bibliographicIssueDate":"2002-09-20","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"89(2002-MPS-041)","bibliographicVolumeNumber":"2002"}]},"relation_version_is_last":true,"weko_creator_id":"1"},"created":"2025-01-18T23:02:15.855078+00:00","id":33420,"links":{}}