{"id":10274,"updated":"2025-01-23T02:57:17.714531+00:00","links":{},"created":"2025-01-18T22:45:13.798839+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00010274","sets":["581:612:619"]},"path":["619"],"owner":"1","recid":"10274","title":["ε 制約遺伝的アルゴリズムによる制約付き最適化"],"pubdate":{"attribute_name":"公開日","attribute_value":"2006-06-15"},"_buckets":{"deposit":"5c810290-69d9-479b-bf67-36c078f4c6dd"},"_deposit":{"id":"10274","pid":{"type":"depid","value":"10274","revision_id":0},"owners":[1],"status":"published","created_by":1},"item_title":"ε 制約遺伝的アルゴリズムによる制約付き最適化","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ε 制約遺伝的アルゴリズムによる制約付き最適化"},{"subitem_title":"Constrained Optimization by the ε Constrained Genetic Algorithm","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"特集：情報処理技術のフロンティア","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2006-06-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"広島市立大学"},{"subitem_text_value":"広島修道大学"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Hiroshima City University","subitem_text_language":"en"},{"subitem_text_value":"Hiroshima Shudo University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/10274/files/IPSJ-JNL4706028.pdf"},"date":[{"dateType":"Available","dateValue":"2008-06-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL4706028.pdf","filesize":[{"value":"191.2 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":"8"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"1fc2d196-f742-486a-a61e-a284f0af767f","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2006 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"高濱, 徹行"},{"creatorName":"阪井, 節子"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Tetsuyuki, Takahama","creatorNameLang":"en"},{"creatorName":"Setsuko, Sakai","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_2_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00116647","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_2_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7764","subitem_source_identifier_type":"ISSN"}]},"item_2_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"進化的アルゴリズムに基づいた制約付き最適化に関する研究が活発に行われている．しかし，従来の方法では探索の安定性が低い，制約領域内の局所解からの脱出が不十分である，目的関数の評価回数が多いという問題があった．本研究では，ε 制約法を遺伝的アルゴリズム（GA）に適用したεGAを提案する．εGA は，均等に親を選択し親と子の上位を次世代に残す選択，一様交叉，Gauss 突然変異，Cauchy 突然変異を採用することにより，安定した局所解に陥りにくい効率的な探索を行うことができる．εGA を13 個の多様な制約付き非線形最適化問題に適用し，他の方法と比較することによりその有効性を示した．","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Researches on constrained optimization using evolutionary algorithms have been actively studied. However, these reseaches have problems that the stability and the efficiency of the search is low and the ability of escaping from local solutions is inadequate. In this study, we propose the εGA, which is defined by applying the ε constrained method to a genetic algorithm. The εGA adopts the selection where parents are chosen equally and next generation is formed by top individuals from parents and children, uniform crossover, Gaussian mutation and Cauchy mutation. The εGA realizes stable and efficient search that can escape local solutions. The advantage of the εGA is shown by applying the εGA to various type of 13 constrained problems and comparing the results to the results by other methods.","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"1871","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicPageStart":"1861","bibliographicIssueDates":{"bibliographicIssueDate":"2006-06-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"6","bibliographicVolumeNumber":"47"}]},"relation_version_is_last":true,"item_2_alternative_title_2":{"attribute_name":"その他タイトル","attribute_value_mlt":[{"subitem_alternative_title":"知識処理"}]},"weko_creator_id":"1"}}