{"updated":"2025-01-19T17:33:58.805929+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00212231","sets":["934:989:10456:10630"]},"path":["10630"],"owner":"44499","recid":"212231","title":["自己適応型差分進化法におけるアルゴリズム構成の事前検証フレームワークによる性能の向上"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-08-10"},"_buckets":{"deposit":"9723e57c-e743-45bf-ad30-802be27d8ead"},"_deposit":{"id":"212231","pid":{"type":"depid","value":"212231","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"自己適応型差分進化法におけるアルゴリズム構成の事前検証フレームワークによる性能の向上","author_link":["541017","541014","541015","541016"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"自己適応型差分進化法におけるアルゴリズム構成の事前検証フレームワークによる性能の向上"},{"subitem_title":"Performance Improvement with Prior-validation Framework for Algorithmic Configuration on Self-adaptive Differential Evolution","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[オリジナル論文] 自己適応型差分進化法，事前検証，高計算コストな最適化問題","subitem_subject_scheme":"Other"}]},"item_type_id":"3","publish_date":"2021-08-10","item_3_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"横浜国立大学"},{"subitem_text_value":"横浜国立大学"}]},"item_3_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Yokohama National University","subitem_text_language":"en"},{"subitem_text_value":"Yokohama National University","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/212231/files/IPSJ-TOM1403006.pdf","label":"IPSJ-TOM1403006.pdf"},"date":[{"dateType":"Available","dateValue":"2023-08-10"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-TOM1403006.pdf","filesize":[{"value":"1.1 MB"}],"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":"dcc48012-d1d4-4988-a472-1942451f3383","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_3_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"西原, 慧"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中田, 雅也"}],"nameIdentifiers":[{}]}]},"item_3_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Kei, Nishihara","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masaya, Nakata","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_3_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11464803","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_3_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7780","subitem_source_identifier_type":"ISSN"}]},"item_3_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"自己適応型差分進化法は，アルゴリズム構成を試行錯誤的に調整するため，少ない解評価回数では性能が十分に改善しない．本論文は，調整されたアルゴリズム構成の事前検証によって，試行錯誤的な調整を削減し，少ない解評価回数で高い性能を実現することを目的とする．また，提案する事前検証フレームワークは高い手法的汎用性があり，スケール係数，交叉率，突然変異・交叉戦略を個体ごとに調整する自己適応型差分進化法に適用できる．ベンチマーク問題を用いた実験では，代表手法であるjDEとSaDE，JADEにそれぞれ提案手法を適用した結果，通常よりも少ない数千オーダの解評価回数において，その性能が改善することを示す．これは，自己適応型差分進化法が不得意とする高計算コストな問題において，提案手法がこれに展開できる汎用的な方法論となりうることを示すものである．","subitem_description_type":"Other"}]},"item_3_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Self-adaptive differential evolution approaches (self-adaptive DEs) often suffer to boost their performances under a limited number of fitness evaluations, since they heavily rely on the trial-and-error process required to adapt algorithmic configurations. In order to enhance the performance in early generations, this paper presents a generalized prior-validation framework for algorithmic configurations, which can be applicable to major variants of self-adaptive DEs that adapt the scaling factor, the crossover rate, and/or the mutation/crossover strategies for each individual. Experimental results on benchmark problems show that the proposed method successfully boosts the performances of jDE, SaDE, and JADE. Thus, the proposed method reveals a possibility of self-adaptive DEs toward computationally-expensive optimization problems where self-adaptive DEs have had a difficulty.","subitem_description_type":"Other"}]},"item_3_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"67","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌数理モデル化と応用（TOM）"}],"bibliographicPageStart":"51","bibliographicIssueDates":{"bibliographicIssueDate":"2021-08-10","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"3","bibliographicVolumeNumber":"14"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:13:13.593912+00:00","id":212231,"links":{}}