| Contemporary symbolic regression methods and their relative performance W La Cava, B Burlacu, M Virgolin, M Kommenda, P Orzechowski, ... Advances in neural information processing systems 2021 (DB1), 1, 2021 | 241 | 2021 |
| 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 | 114 | 2020 |
| 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 | 94 | 2020 |
| 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 | 47 | 2022 |
| 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 | 46 | 2014 |
| 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 | 40 | 2016 |
| 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 | 29 | 2020 |
| Visualization of genetic lineages and inheritance information in genetic programming B Burlacu, M Affenzeller, M Kommenda, S Winkler, G Kronberger Proceedings of the 15th annual conference companion on Genetic and …, 2013 | 29 | 2013 |
| Shape-constrained multi-objective genetic programming for symbolic regression C Haider, FO de Franca, B Burlacu, G Kronberger Applied Soft Computing 132, 109855, 2023 | 17 | 2023 |
| White box vs. black box modeling: On the performance of deep learning, random forests, and symbolic regression in solving regression problems M Affenzeller, B Burlacu, V Dorfer, S Dorl, G Halmerbauer, ... Computer Aided Systems Theory–EUROCAST 2019: 17th International Conference …, 2020 | 15 | 2020 |
| Dynamic observation of genotypic and phenotypic diversity for different symbolic regression gp variants M Affenzeller, SM Winkler, B Burlacu, G Kronberger, M Kommenda, ... Proceedings of the genetic and evolutionary computation conference companion …, 2017 | 14 | 2017 |
| Sliding window symbolic regression for detecting changes of system dynamics SM Winkler, M Affenzeller, G Kronberger, M Kommenda, B Burlacu, ... Genetic Programming Theory and Practice XII, 91-107, 2015 | 13 | 2015 |
| Genetic programming with data migration for symbolic regression M Kommenda, M Affenzeller, B Burlacu, G Kronberger, SM Winkler Proceedings of the Companion Publication of the 2014 Annual Conference on …, 2014 | 13 | 2014 |
| Using shape constraints for improving symbolic regression models C Haider, FO de França, B Burlacu, G Kronberger arXiv preprint arXiv:2107.09458, 2021 | 11 | 2021 |
| Parsimony measures in multi-objective genetic programming for symbolic regression B Burlacu, G Kronberger, M Kommenda, M Affenzeller Proceedings of the genetic and evolutionary computation conference companion …, 2019 | 11 | 2019 |
| Interpretable symbolic regression for data science: analysis of the 2022 competition FO de França, M Virgolin, M Kommenda, MS Majumder, M Cranmer, ... arXiv preprint arXiv:2304.01117, 2023 | 10 | 2023 |
| Methods for genealogy and building block analysis in genetic programming B Burlacu, M Affenzeller, S Winkler, M Kommenda, G Kronberger Computational Intelligence and Efficiency in Engineering Systems, 61-74, 2015 | 10 | 2015 |
| Genetic Programming Theory and Practice XVII L Kammerer, G Kronberger, B Burlacu, SM Winkler, M Kommenda, ... Springer, Cham, 2020 | 9 | 2020 |
| Comparing optimistic and pessimistic constraint evaluation in shape-constrained symbolic regression C Haider, FO de França, G Kronberger, B Burlacu Proceedings of the Genetic and Evolutionary Computation Conference, 938-945, 2022 | 8 | 2022 |
| Contemporary symbolic regression methods and their relative performance, 2021 W La Cava, P Orzechowski, B Burlacu, FO de França, M Virgolin, Y Jin, ... URL https://arxiv. org/abs/2107.14351, 2021 | 8 | 2021 |
Michael KommendaTietoevryVerified email at tietoevry.com
