Exploiting active subspaces in global optimization: How complex is your problem?

Pramudita Satria Palar, Koji Shimoyama

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

When applying optimization method to a real-world problem, the possession of prior knowledge and preliminary analysis on the landscape of a global optimization problem can give us an insight into the complexity of the problem. This knowledge can better inform us in deciding what optimization method should be used to tackle the problem. However, this analysis becomes problematic when the dimensionality of the problem is high. This paper presents a framework to take a deeper look at the global optimization problem to be tackled: by analyzing the low-dimensional representation of the problem through discovering the active subspaces of the given problem. The virtue of this is that the problem's complexity can be visualized in a one or two-dimensional plot, thus allow one to get a better grip about the problem's diffculty. One could then have a better idea regarding the complexity of their problem to determine the choice of global optimizer or what surrogate-model type to be used. Furthermore, we also demonstrate how the active subspacescan be used to perform design exploration and analysis.

本文言語English
ホスト出版物のタイトルGECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion
出版社Association for Computing Machinery, Inc
ページ1487-1494
ページ数8
ISBN(電子版)9781450349390
DOI
出版ステータスPublished - 2017 7 15
イベント2017 Genetic and Evolutionary Computation Conference Companion, GECCO 2017 - Berlin, Germany
継続期間: 2017 7 152017 7 19

出版物シリーズ

名前GECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion

Other

Other2017 Genetic and Evolutionary Computation Conference Companion, GECCO 2017
CountryGermany
CityBerlin
Period17/7/1517/7/19

ASJC Scopus subject areas

  • Software
  • Computational Theory and Mathematics
  • Computer Science Applications

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