{"updated":"2025-01-22T15:42:24.481865+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00033422","sets":["1164:2735:2772:2774"]},"path":["2774"],"owner":"1","recid":"33422","title":["エージェント指向自己適応遺伝アルゴリズム"],"pubdate":{"attribute_name":"公開日","attribute_value":"2002-09-20"},"_buckets":{"deposit":"4a9fe7e1-a57b-4893-ad82-20538d80ac78"},"_deposit":{"id":"33422","pid":{"type":"depid","value":"33422","revision_id":0},"owners":[1],"status":"published","created_by":1},"item_title":"エージェント指向自己適応遺伝アルゴリズム","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"エージェント指向自己適応遺伝アルゴリズム"},{"subitem_title":"Agent Oriented Self Adaptive Genetic Algorithm","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":"奈良先端科学技術大学院大学情報科学研究科"},{"subitem_text_value":"奈良先端科学技術大学院大学情報科学研究科"},{"subitem_text_value":"奈良先端科学技術大学院大学情報科学研究科"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information Science Nara Institute of Science and Technology","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science Nara Institute of Science and Technology","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science Nara Institute of Science and 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/33422/files/IPSJ-MPS02041006.pdf"},"date":[{"dateType":"Available","dateValue":"2004-09-20"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MPS02041006.pdf","filesize":[{"value":"698.9 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":"993a5b5f-425d-49ca-8284-5c068a17da12","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":"村田, 佳洋"},{"creatorName":"柴田, 直樹"},{"creatorName":"伊藤, 実"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yoshihiro, Murata","creatorNameLang":"en"},{"creatorName":"Naoki, Shibata","creatorNameLang":"en"},{"creatorName":"Minoru, Ito","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":"遺伝アルゴリズム(Genetic Algorithm 以下GA)の探索効率は，突然変異率や交叉率といったパラメータによって大きく左右される．しかし，多くのパラメータの調整を人手で行うのは困難である．そこでパラメータを自動的に調整する様々な適応GAが提案されている．従来の適応GAでは少数のパラメータしか適応させないものがほとんどであった．また，多数のパラメータを適応させる適応GAもいくつかあるが，その大部分が大きな計算量を必要としていた．　本稿ではエージェント指向の手法によりメタGAと環境分散型並列GAを組み合わせ，多数のパラメータの組み合わせを同時に適応させつつ探索を行うエージェント指向自己適応遺伝アルゴリズムを提案した．また評価実験を行い，4つのパラメータを同時に合理的な計算量で適応させることができた．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Efficiency of Genetic Algorithms (GAs) depends largely on parameters such as crossover rate and mutation rate.  In general, however, it is difficult to adjust those parameters manually. Although there are a few researches about adaptive GAs for adjusting multiple parameters, they require extremely large computation costs. In this paper, we propose a new algorithm based on multi agent techniques which combines existing meta-GA techniques and GA with distributed environment scheme. %%Our algorithm can adjust parameters while exploring solutions. Through some simulations, we have confirmed that the proposedalgorithm can adapt multiple parameters in reasonable computation costs.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"24","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":"21","bibliographicIssueDates":{"bibliographicIssueDate":"2002-09-20","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"89(2002-MPS-041)","bibliographicVolumeNumber":"2002"}]},"relation_version_is_last":true,"weko_creator_id":"1"},"created":"2025-01-18T23:02:15.943319+00:00","id":33422,"links":{}}