research data . Dataset . 2020

An Empirical Comparison of Meta-Modeling Techniques for Robust Design Optimization

Sibghat Ullah; Hao Wang; Stefan Menzel; Thomas Bäck; Bernhard Sendhoff;
Open Access English
  • Published: 01 Jan 2020
  • Publisher: Zenodo
Abstract
<p>This is the data and source code used in the paper below:</p> <p>Sibghat Ullah, Hao Wang, Stefan Menzel, Bernhard Sendhoff and Thomas B&auml;ck, &ldquo;An Empirical Comparison of Meta-Modeling Techniques for Robust Design Optimization&rdquo;, in 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 6-9 December 2019, doi:&nbsp;10.1109/SSCI44817.2019.9002805</p> <p>This research investigates the potential of using meta-modeling techniques in the context of robust optimization namely optimization under uncertainty/noise. A systematic empirical comparison is performed for evaluating and comparing different meta-modeling techniques for r...
Subjects
free text keywords: meta-modeling, surrogate-assisted optimization, robust optimization, quality engineering, machine learning
Funded by
EC| ECOLE
Project
ECOLE
Experience-based Computation: Learning to Optimise
  • Funder: European Commission (EC)
  • Project Code: 766186
  • Funding stream: H2020 | MSCA-ITN-EID
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Dataset . 2020
Provider: Zenodo
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