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Jörg Stork
Jörg Stork
Senior Data Scientist, Otto Fuchs KG
Bestätigte E-Mail-Adresse bei otto-fuchs.com
Titel
Zitiert von
Zitiert von
Jahr
Comparison of different methods for univariate time series imputation in R
S Moritz, A Sardá, T Bartz-Beielstein, M Zaefferer, J Stork
arXiv preprint arXiv:1510.03924, 2015
1652015
Efficient global optimization for combinatorial problems
M Zaefferer, J Stork, M Friese, A Fischbach, B Naujoks, T Bartz-Beielstein
Proceedings of the 2014 annual conference on genetic and evolutionary …, 2014
662014
A new taxonomy of global optimization algorithms
J Stork, AE Eiben, T Bartz-Beielstein
Natural Computing, 1-24, 2020
332020
Distance measures for permutations in combinatorial efficient global optimization
M Zaefferer, J Stork, T Bartz-Beielstein
International Conference on Parallel Problem Solving from Nature, 373-383, 2014
312014
Open issues in surrogate-assisted optimization
J Stork, M Friese, M Zaefferer, T Bartz-Beielstein, A Fischbach, ...
High-performance simulation-based optimization, 225-244, 2020
272020
Comparison of parallel surrogate-assisted optimization approaches
F Rehbach, M Zaefferer, J Stork, T Bartz-Beielstein
Proceedings of the Genetic and Evolutionary Computation Conference, 1348-1355, 2018
172018
Improving neuroevolution efficiency by surrogate model-based optimization with phenotypic distance kernels
J Stork, M Zaefferer, T Bartz-Beielstein
International Conference on the Applications of Evolutionary Computation …, 2019
132019
SVM ensembles are better when different kernel types are combined
J Stork, R Ramos, P Koch, W Konen
Data Science, Learning by Latent Structures, and Knowledge Discovery, 191-201, 2015
132015
Data preprocessing: A new algorithm for univariate imputation designed specifically for industrial needs
S Chandrasekaran, M Zaefferer, S Moritz, J Stork, M Friese, A Fischbach, ...
122016
Tuning multi-objective optimization algorithms for cyclone dust separators
M Zaefferer, B Breiderhoff, B Naujoks, M Friese, J Stork, A Fischbach, ...
Proceedings of the 2014 Annual Conference on Genetic and Evolutionary …, 2014
122014
rgp: R genetic programming framework
O Flasch, O Mersmann, T Bartz-Beielstein, J Stork, M Zaefferer
R. package version0, 4-1, 2014
122014
Comparison of different methods for univariate time series imputation in R. arXiv 2015
S Moritz, A Sardá, T Bartz-Beielstein, M Zaefferer, J Stork
arXiv preprint arXiv:1510.03924, 0
12
CAAI—a cognitive architecture to introduce artificial intelligence in cyber-physical production systems
A Fischbach, J Strohschein, A Bunte, J Stork, H Faeskorn-Woyke, N Moriz, ...
The International Journal of Advanced Manufacturing Technology 111 (1), 609-626, 2020
92020
Surrogate models for enhancing the efficiency of neuroevolution in reinforcement learning
J Stork, M Zaefferer, T Bartz-Beielstein, AE Eiben
Proceedings of the genetic and evolutionary computation conference, 934-942, 2019
92019
Linear combination of distance measures for surrogate models in genetic programming
M Zaefferer, J Stork, O Flasch, T Bartz-Beielstein
International Conference on Parallel Problem Solving from Nature, 220-231, 2018
92018
Rgp: R Genetic Programming Framework. R Package Version 0.2-4, 2011
O Flasch, O Mersmann, T Bartz-Beielstein, J Stork
9
From real world data to test functions
A Fischbach, M Zaefferer, J Stork, M Friese, T Bartz-Beielstein
72016
Surrogates for hierarchical search spaces: the wedge-kernel and an automated analysis
D Horn, J Stork, NJ Schüßler, M Zaefferer
Proceedings of the genetic and evolutionary computation conference, 916-924, 2019
62019
Prediction of neural network performance by phenotypic modeling
A Hagg, M Zaefferer, J Stork, A Gaier
Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2019
52019
Distance-based kernels for surrogate model-based neuroevolution
J Stork, M Zaefferer, T Bartz-Beielstein
arXiv preprint arXiv:1807.07839, 2018
52018
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