| 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 | 97 | 2015 |
| 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 | 45 | 2014 |
| 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 | 22 | 2014 |
| rgp: R genetic programming framework O Flasch, O Mersmann, T Bartz-Beielstein, J Stork, M Zaefferer R package version 0.4-1, 2014 | 18 | 2014 |
| 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 | 11 | 2015 |
| 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 | 10 | 2018 |
| 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 | 9 | 2014 |
| 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 | 8 | 2018 |
| 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 | 7 | 2020 |
| A new taxonomy of continuous global optimization algorithms J Stork, AE Eiben, T Bartz-Beielstein arXiv preprint arXiv:1808.08818, 2018 | 7 | 2018 |
| From real world data to test functions A Fischbach, M Zaefferer, J Stork, M Friese, T Bartz-Beielstein | 7 | 2016 |
| Distance-based kernels for surrogate model-based neuroevolution J Stork, M Zaefferer, T Bartz-Beielstein arXiv preprint arXiv:1807.07839, 2018 | 5 | 2018 |
| Gecco 2017 industrial challenge: Monitoring of drinking water quality S Chandrasekaran, M Freise, J Stork, M Rebolledo, T Bartz-Beielstein | 5 | 2017 |
| UniFIeD Univariate Frequency-based Imputation for Time Series Data M Friese, J Stork, R Ramos Guerra, T Bartz-Beielstein, S Thaker, O Flasch, ... | 5 | 2013 |
| Simulation and optimization of cyclone dust separators B Breiderhoff, T Bartz-Beielstein, B Naujoks, M Zaefferer, A Fischbach, ... | 5 | 2013 |
| 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 | 5 | |
| A new taxonomy of global optimization algorithms J Stork, AE Eiben, T Bartz-Beielstein Natural Computing, 1-24, 2020 | 4 | 2020 |
| 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 | 4 | 2019 |
| 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 | 4 | 2019 |
| Surrogate-Assisted Learning of Neural Networks J Stork, M Zaefferer, A Fischbach, F Rehbach, T Bartz-Beielstein | 4 | 2017 |
Thomas Bartz-BeielsteinTH KoelnBestätigte E-Mail-Adresse bei th-koeln.de
