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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

Visualization of Search Process and Improvement of Search Performance in Multi-Objective Genetic Algorithm

http://hdl.handle.net/2237/9486
http://hdl.handle.net/2237/9486
27add379-453f-425a-8118-2f5080100ae9
名前 / ファイル ライセンス アクション
yoshikawa_2.pdf yoshikawa_2.pdf (254.0 kB)
Item type 学術雑誌論文 / Journal Article(1)
公開日 2008-02-26
タイトル
タイトル Visualization of Search Process and Improvement of Search Performance in Multi-Objective Genetic Algorithm
言語 en
著者 Yamashiro, Daisuke

× Yamashiro, Daisuke

WEKO 22025

en Yamashiro, Daisuke

Search repository
Yoshikawa, Tomohiro

× Yoshikawa, Tomohiro

WEKO 22026

en Yoshikawa, Tomohiro

Search repository
Furuhashi, Takeshi

× Furuhashi, Takeshi

WEKO 22027

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 Copyright © 2006 IEEE. Reprinted from (relevant publication info). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Nagoya University’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org.
抄録
内容記述 Performance in searching solutions by Multi-Objective Genetic Algorithm (MOGA) depends on genetic operators and/or their parameters. For comparison of the performance with some genetic operators and/or parameters, it has been usually employed the transitions of fitness values through actual applications or the number/performance of acquired Pareto solutions in multi-optimization problems. This paper proposes a visualizing method of search process for MOGA, which can visualize relative distances among chromosomes in search process and give information of not only the performance but also the effects of the genetic operations such as the diversity of chromosomes. This method uses Self-Organizing Map (SOM) for the visualization. This paper applies Non Dominated Sorting Genetic Algorithm-Ⅱ (NSGA-Ⅱ) to ZDT2 and FON test functions and shows obtained nondominated solutions and visualization results. This paper also shows that the visualized data enables us to interpret the differences in search processes and to get new information to determine efficient genetic operators and their parameters.
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 VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
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.2006.1688439
ISBN
関連タイプ isPartOf
識別子タイプ ISBN
関連識別子 0-7803-9487-9
書誌情報 en : IEEE Congress on Evolutionary Computation

p. 1151-1156, 発行日 2006
フォーマット
application/pdf
著者版フラグ
値 publisher
URI
識別子 http://hdl.handle.net/2237/9486
識別子タイプ HDL
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