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  1. B200 工学部/工学研究科
  2. B200a 雑誌掲載論文
  3. 学術雑誌

Study on Effect of MOGA with Interactive Island Model using Visualization

http://hdl.handle.net/2237/14440
http://hdl.handle.net/2237/14440
7f13152b-a646-4c46-80a2-d41adc853dd0
名前 / ファイル ライセンス アクション
1052.pdf 1052.pdf (1.1 MB)
Item type 学術雑誌論文 / Journal Article(1)
公開日 2011-02-07
タイトル
タイトル Study on Effect of MOGA with Interactive Island Model using Visualization
言語 en
著者 Yamamoto, Masafumi

× Yamamoto, Masafumi

WEKO 39479

en Yamamoto, Masafumi

Search repository
Yoshikawa, Tomohiro

× Yoshikawa, Tomohiro

WEKO 39480

en Yoshikawa, Tomohiro

Search repository
Furuhashi, Takeshi

× Furuhashi, Takeshi

WEKO 39481

en Furuhashi, Takeshi

Search repository
アクセス権
アクセス権 open access
アクセス権URI http://purl.org/coar/access_right/c_abf2
権利
言語 en
権利情報 © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
抄録
内容記述 Genetic Algorithm is one of the most effective optimization algorithms, on which a lot of studies have been reported. Some studies on the application of island model, which is one of the representative methods to keep a diversity of solutions, to Multi-Objective Genetic Algorithm (MOGA) have been conducted. In MOGA, it is difficult to find the solutions which satisfy all objective functions because of their tradeoff. Especially when there are many objective functions, it is obvious that it needs a lot of time to search for effective Pareto solutions and find them. This paper proposes the interactive way of addition and deletion of islands to the original ones based on user's requirements with the visualization of acquired solutions in island model for MOGA. This paper applies the proposed method to Nurse Scheduling Problem (NSP) using the visualization by Principal Component Analysis (PCA). Through the experiment, it is confirmed that an interactive tuning of the weights for the objective functions leaded to the acquisition of better Pareto solutions which a user wants while they are difficult to be acquired by the prepared weights.
言語 en
内容記述タイプ Abstract
出版者
言語 en
出版者 IEEE
言語
言語 eng
資源タイプ
資源タイプresource http://purl.org/coar/resource_type/c_6501
タイプ journal article
出版タイプ
出版タイプ AM
出版タイプResource http://purl.org/coar/version/c_ab4af688f83e57aa
DOI
関連タイプ isVersionOf
識別子タイプ DOI
関連識別子 https://doi.org/10.1109/CEC.2010.5585950
ISBN
関連タイプ isPartOf
識別子タイプ ISBN
関連識別子 978-1-4244-6909-3
書誌情報 en : IEEE Congress on Evolutionary Computation (CEC)

p. 1-6, 発行日 2010
著者版フラグ
値 author
URI
識別子 http://hdl.handle.net/2237/14440
識別子タイプ HDL
URI
識別子 http://dx.doi.org/10.1109/CEC.2010.5585950
識別子タイプ DOI
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