Dimensionality-reduction frameworks for computationally expensive problems

Yoel Tenne, Kazuhiro Izui, Shinji Nishiwaki

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

Real-world design optimization problems are typically computationally- expensive and to address this various model-assisted evolutionary frameworks have been proposed. However, often such problems are also high-dimensional and in such settings models tend to have poor accuracy and thus degrade the optimization search. To address this we propose two complementary dimensionality-reduction frameworks for evolutionary model-assisted optimization: one uses variable-selection to identify an important subset of the original variables while the other uses topological mapping to project the high-dimensional data to a lower-dimension. Performance analysis with both mathematical test functions and a problem of airfoil shape optimization evaluates the efficacy of the frameworks.

Original languageEnglish
Title of host publication2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
DOIs
StatePublished - 2010
Externally publishedYes
Event2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010 - Barcelona, Spain
Duration: 18 Jul 201023 Jul 2010

Publication series

Name2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010

Conference

Conference2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
Country/TerritorySpain
CityBarcelona
Period18/07/1023/07/10

Fingerprint

Dive into the research topics of 'Dimensionality-reduction frameworks for computationally expensive problems'. Together they form a unique fingerprint.

Cite this