| Genetic algorithms O Kramer, O Kramer Genetic algorithm essentials, 11-19, 2017 | 892 | 2017 |
| K-nearest neighbors O Kramer, O Kramer Dimensionality reduction with unsupervised nearest neighbors, 13-23, 2013 | 830* | 2013 |
| Scikit-learn O Kramer, O Kramer Machine learning for evolution strategies, 45-53, 2016 | 401 | 2016 |
| Long-term research challenges in wind energy–a research agenda by the European Academy of Wind Energy GAM Van Kuik, J Peinke, R Nijssen, D Lekou, J Mann, JN Sørensen, ... Wind energy science 1 (1), 1-39, 2016 | 289 | 2016 |
| Machine learning ensembles for wind power prediction J Heinermann, O Kramer Renewable Energy 89, 671-679, 2016 | 245 | 2016 |
| Comparing support vector regression for PV power forecasting to a physical modeling approach using measurement, numerical weather prediction, and cloud motion data B Wolff, J Kühnert, E Lorenz, O Kramer, D Heinemann Solar Energy 135, 197-208, 2016 | 235 | 2016 |
| A Review of Constraint‐Handling Techniques for Evolution Strategies O Kramer Applied Computational Intelligence and Soft Computing 2010 (1), 185063, 2010 | 197 | 2010 |
| Energy informatics: current and future research directions C Goebel, HA Jacobsen, V Del Razo, C Doblander, J Rivera, J Ilg, C Flath, ... Wirtschaftsinformatik 56, 31-39, 2014 | 135 | 2014 |
| Evolutionary self-adaptation: a survey of operators and strategy parameters O Kramer Evolutionary Intelligence 3, 51-65, 2010 | 135 | 2010 |
| Machine learning for evolution strategies O Kramer Springer, 2016 | 119 | 2016 |
| Derivative-free optimization O Kramer, DE Ciaurri, S Koziel Computational optimization, methods and algorithms, 61-83, 2011 | 111 | 2011 |
| Self-adaptive heuristics for evolutionary computation O Kramer Springer, 2008 | 90 | 2008 |
| Short-term wind energy forecasting using support vector regression O Kramer, F Gieseke Soft computing models in industrial and environmental applications, 6th …, 2011 | 81 | 2011 |
| Wind power prediction with machine learning NA Treiber, J Heinermann, O Kramer Computational sustainability, 13-29, 2016 | 76 | 2016 |
| Computational intelligence: eine Einführung O Kramer Springer-Verlag, 2009 | 73 | 2009 |
| Acute lymphoblastic leukemia classification from microscopic images using convolutional neural networks J Prellberg, O Kramer ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging: Select …, 2019 | 71 | 2019 |
| Fast and simple gradient-based optimization for semi-supervised support vector machines F Gieseke, A Airola, T Pahikkala, O Kramer Neurocomputing 123, 23-32, 2014 | 71 | 2014 |
| Evolution of human-competitive agents in modern computer games S Priesterjahn, O Kramer, A Weimer, A Goebels 2006 IEEE International Conference on Evolutionary Computation, 777-784, 2006 | 71 | 2006 |
| Statistical learning for short-term photovoltaic power predictions B Wolff, E Lorenz, O Kramer Computational sustainability, 31-45, 2016 | 70 | 2016 |
| Optimization of elastic properties and weaving patterns of woven composites IAA Bakar, O Kramer, S Bordas, T Rabczuk Composite Structures 100, 575-591, 2013 | 65 | 2013 |
