| Better GP benchmarks: community survey results and proposals DR White, J McDermott, M Castelli, L Manzoni, BW Goldman, ... Genetic programming and evolvable machines 14 (1), 3-29, 2013 | 299 | 2013 |
| Architecture and design of the HeuristicLab optimization environment S Wagner, G Kronberger, A Beham, M Kommenda, A Scheibenpflug, ... Advanced methods and applications in computational intelligence, 197-261, 2014 | 216 | 2014 |
| Operon C++ an efficient genetic programming framework for symbolic regression B Burlacu, G Kronberger, M Kommenda Proceedings of the 2020 genetic and evolutionary computation conference …, 2020 | 188 | 2020 |
| Parameter identification for symbolic regression using nonlinear least squares M Kommenda, B Burlacu, G Kronberger, M Affenzeller Genetic Programming and Evolvable Machines 21 (3), 471-501, 2020 | 163 | 2020 |
| Data based prediction of blood glucose concentrations using evolutionary methods JI Hidalgo, JM Colmenar, G Kronberger, SM Winkler, O Garnica, ... Journal of medical systems 41 (9), 142, 2017 | 108 | 2017 |
| Shape-constrained symbolic regression—improving extrapolation with prior knowledge G Kronberger, FO de França, B Burlacu, C Haider, M Kommenda Evolutionary computation 30 (1), 75-98, 2022 | 91 | 2022 |
| Effects of constant optimization by nonlinear least squares minimization in symbolic regression M Kommenda, G Kronberger, S Winkler, M Affenzeller, S Wagner Proceedings of the 15th annual conference companion on Genetic and …, 2013 | 81 | 2013 |
| Using FE calculations and data-based system identification techniques to model the nonlinear behavior of PMSMs G Bramerdorfer, SM Winkler, M Kommenda, G Weidenholzer, S Silber, ... IEEE Transactions on Industrial Electronics 61 (11), 6454-6462, 2014 | 65 | 2014 |
| Symbolic regression G Kronberger, B Burlacu, M Kommenda, SM Winkler, M Affenzeller Chapman and Hall/CRC, 2024 | 58 | 2024 |
| Application of symbolic regression for constitutive modeling of plastic deformation E Kabliman, AH Kolody, J Kronsteiner, M Kommenda, G Kronberger Applications in Engineering Science 6, 100052, 2021 | 58 | 2021 |
| Advanced methods and applications in computational intelligence S Wagner, G Kronberger, A Beham, M Kommenda, A Scheibenpflug, ... Topics in Intelligent Engineering and Informatics 6, 197-261, 2014 | 54 | 2014 |
| Parameter meta-optimization of metaheuristic optimization algorithms C Neumüller, S Wagner, G Kronberger, M Affenzeller International Conference on Computer Aided Systems Theory, 367-374, 2011 | 52 | 2011 |
| Evolving simple symbolic regression models by multi-objective genetic programming M Kommenda, G Kronberger, M Affenzeller, SM Winkler, B Burlacu Genetic Programming Theory and Practice XIII, 1-19, 2016 | 50 | 2016 |
| Gaining deeper insights in symbolic regression M Affenzeller, SM Winkler, G Kronberger, M Kommenda, B Burlacu, ... Genetic programming theory and practice xi, 175-190, 2014 | 50 | 2014 |
| Benefits of plugin-based heuristic optimization software systems S Wagner, S Winkler, E Pitzer, G Kronberger, A Beham, R Braune, ... International Conference on Computer Aided Systems Theory, 747-754, 2007 | 50 | 2007 |
| Shape-constrained multi-objective genetic programming for symbolic regression C Haider, FO de Franca, B Burlacu, G Kronberger Applied Soft Computing 132, 109855, 2023 | 40 | 2023 |
| Symbolic regression by exhaustive search: Reducing the search space using syntactical constraints and efficient semantic structure deduplication L Kammerer, G Kronberger, B Burlacu, SM Winkler, M Kommenda, ... Genetic programming theory and practice XVII, 79-99, 2020 | 40 | 2020 |
| Macro-economic time series modeling and interaction networks G Kronberger, S Fink, M Kommenda, M Affenzeller European Conference on the Applications of Evolutionary Computation, 101-110, 2011 | 40 | 2011 |
| Complexity measures for multi-objective symbolic regression M Kommenda, A Beham, M Affenzeller, G Kronberger International Conference on Computer Aided Systems Theory, 409-416, 2015 | 39 | 2015 |
| Continuous improvement and adaptation of predictive models in smart manufacturing and model management F Bachinger, G Kronberger, M Affenzeller IET Collaborative Intelligent Manufacturing 3 (1), 48-63, 2021 | 33 | 2021 |
Michael AffenzellerUniversity of Applied Sciences Upper AustriaVerified email at fh-ooe.at
