Visualization and data mining of Pareto solutions using Self-Organizing Map

Shigeru Obayashi, Daisuke Sasaki

研究成果: Chapter

121 被引用数 (Scopus)

抄録

Self-Organizing Maps (SOMs) have been used to visualize tradeoffs of Pareto solutions in the objective function space for engineering design obtained by Evolutionary Computation. Furthermore, based on the codebook vectors of cluster-averaged values of respective design variables obtained from the SOM, the design variable space is mapped onto another SOM. The resulting SOM generates clusters of design variables, which indicate roles of the design variables for design improvements and tradeoffs. These processes can be considered as data mining of the engineering design. Data mining examples are given for supersonic wing design and supersonic wing-fuselage design.

本文言語English
ホスト出版物のタイトルLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
編集者Carlos M. Fonseca, Peter J. Fleming, Eckart Zitzler, Lothar Thiele, Kalyanmoy Deb
出版社Springer Verlag
ページ796-809
ページ数14
ISBN(印刷版)3540018697, 9783540018698
DOI
出版ステータスPublished - 2003

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
2632
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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