Thomas Bartz-Beielstein
Cologne University of Applied Sciences
183 Papers
677 Citations
Thomas Bartz-Beielstein is an academic researcher from Cologne University of Applied Sciences. The author has contributed to research in topics: Computer science & Optimization problem. The author has an hindex of 25, co-authored 166 publications. Previous affiliations of Thomas Bartz-Beielstein include Technical University of Dortmund & University of Cologne.
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Papers
imputeTS: Time Series Missing Value Imputation in R
TL;DR: This paper provides an introduction to the imputeTS package and its provided algorithms and tools, and gives a short overview about univariate time series imputation in R.
Experimental Methods for the Analysis of Optimization Algorithms
Thomas Bartz-Beielstein,Marco Chiarandini,Lus Paquete,Mike Preuss +3 more
- 03 Nov 2010
TL;DR: In this article, the main issues in the experimental analysis of algorithms are discussed, and the experimental cycle of algorithm development is discussed, as well as statistical distributions of algorithm performance in terms of solution quality, runtime and other measures.
440
Model-based methods for continuous and discrete global optimization
Thomas Bartz-Beielstein,Martin Zaefferer +1 more
- 01 Jun 2017
TL;DR: A taxonomy is introduced, which is useful as a guideline for selecting adequate model-based optimization tools and a new approach for combining surrogate information via stacking is proposed in the third part.
•Posted Content
Comparison of different Methods for Univariate Time Series Imputation in R.
TL;DR: The results show that either an interpolation with seasonal kalman filter from the zoo package or a linear interpolation on seasonal loess decomposed data from the forecast package were the most effective methods for dealing with missing data in most of the scenarios assessed in this paper.
141
•Posted Content
Benchmarking in Optimization: Best Practice and Open Issues
Thomas Bartz-Beielstein,Carola Doerr,Jakob Bossek,Sowmya Chandrasekaran,Tome Eftimov,Andreas Fischbach,Pascal Kerschke,Manuel López-Ibáñez,Katherine M. Malan,Jason H. Moore,Boris Naujoks,Patryk Orzechowski,Vanessa Volz,Markus Wagner,Thomas Weise +14 more
TL;DR: The article discusses eight essential topics in benchmarking: clearly stated goals, well-specified problems, suitable algorithms, adequate performance measures, thoughtful analysis, effective and efficient designs, comprehensible presentations, and guaranteed reproducibility.
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