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

Shigeru Obayashi, Daisuke Sasaki

Research output: Chapter in Book/Report/Conference proceedingChapter

121 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsCarlos M. Fonseca, Peter J. Fleming, Eckart Zitzler, Lothar Thiele, Kalyanmoy Deb
PublisherSpringer Verlag
Pages796-809
Number of pages14
ISBN (Print)3540018697, 9783540018698
DOIs
Publication statusPublished - 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2632
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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