| 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 | 220 | 2015 |
| A new taxonomy of global optimization algorithms J Stork, AE Eiben, T Bartz-Beielstein Natural Computing 21 (2), 219-242, 2022 | 90 | 2022 |
| 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 | 77 | 2014 |
| 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 | 51 | 2020 |
| Distance measures for permutations in combinatorial efficient global optimization M Zaefferer, J Stork, T Bartz-Beielstein Parallel Problem Solving from Nature–PPSN XIII: 13th International …, 2014 | 35 | 2014 |
| 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 | 30 | 2018 |
| 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, 609-626, 2020 | 28 | 2020 |
| 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 | 23 | |
| Improving neuroevolution efficiency by surrogate model-based optimization with phenotypic distance kernels J Stork, M Zaefferer, T Bartz-Beielstein Applications of Evolutionary Computation: 22nd International Conference …, 2019 | 17 | 2019 |
| Elevator group control as a constrained multiobjective optimization problem A Vodopija, J Stork, T Bartz-Beielstein, B Filipič Applied Soft Computing 115, 108277, 2022 | 16 | 2022 |
| 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, ... | 16 | 2016 |
| rgp: R genetic programming framework O Flasch, O Mersmann, T Bartz-Beielstein, J Stork, M Zaefferer R. package version0, 4-1, 2014 | 14 | 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 | 13 | 2015 |
| 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 | 12 | 2019 |
| 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 | 12 | 2014 |
| From real world data to test functions A Fischbach, M Zaefferer, J Stork, M Friese, T Bartz-Beielstein | 11 | 2016 |
| Linear combination of distance measures for surrogate models in genetic programming M Zaefferer, J Stork, O Flasch, T Bartz-Beielstein Parallel Problem Solving from Nature–PPSN XV: 15th International Conference …, 2018 | 10 | 2018 |
| 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 | 9 | 2019 |
| Rgp: R Genetic Programming Framework. R Package Version 0.2-4, 2011 O Flasch, O Mersmann, T Bartz-Beielstein, J Stork | 9 | |
| Behavior-based neuroevolutionary training in reinforcement learning J Stork, M Zaefferer, N Eisler, P Tichelmann, T Bartz-Beielstein, AE Eiben Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2021 | 7 | 2021 |
Thomas Bartz-BeielsteinTH Koeln, Institute for Data Science, Engineering, and Analytics在 th-koeln.de 的电子邮件经过验证
