| 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 | 35 | 2020 |
| 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 | 31 | 2014 |
| Contemporary symbolic regression methods and their relative performance W La Cava, P Orzechowski, B Burlacu, FO de França, M Virgolin, Y Jin, ... arXiv preprint arXiv:2107.14351, 2021 | 26 | 2021 |
| 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 | 24 | 2013 |
| 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 | 22 | 2016 |
| 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 | 15 | 2020 |
| 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 | 11 | 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 | 11 | 2014 |
| 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 | 10 | 2017 |
| 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 | 9 | 2015 |
| 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 | 8 | 2020 |
| On the analysis, classification and prediction of metaheuristic algorithm behavior for combinatorial optimization problems A Scheibenpflug, S Wagner, E Pitzer, B Burlacu, M Affenzeller Proceedings of the 24th European Modeling and Simulation Symposium, EMSS 2012, 2012 | 7 | 2012 |
| 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 | 6 | 2022 |
| Online diversity control in symbolic regression via a fast hash-based tree similarity measure B Burlacu, M Affenzeller, G Kronberger, M Kommenda 2019 IEEE congress on evolutionary computation (CEC), 2175-2182, 2019 | 5 | 2019 |
| 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, ... International Conference on Computer Aided Systems Theory, 288-295, 2019 | 5 | 2019 |
| Cluster analysis of a symbolic regression search space G Kronberger, L Kammerer, B Burlacu, SM Winkler, M Kommenda, ... Genetic Programming Theory and Practice XVI, 85-102, 2019 | 5 | 2019 |
| Multi-population genetic programming with data migration for symbolic regression M Kommenda, M Affenzeller, G Kronberger, B Burlacu, S Winkler Computational Intelligence and Efficiency in Engineering Systems, 75-87, 2015 | 5 | 2015 |
| 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 | 4 | 2019 |
| Similarity-based analysis of population dynamics in genetic programming performing symbolic regression SM Winkler, M Affenzeller, B Burlacu, G Kronberger, M Kommenda, ... Genetic Programming Theory and Practice XIV, 1-17, 2018 | 4 | 2018 |
| Heat treatment process parameter estimation using heuristic optimization algorithms M Kommenda, B Burlacu, R Holecek, A Gebeshuber, M Affenzeller Proceedings of the 27th European Modeling and Simulation Symposium EMSS, 222-228, 2015 | 4 | 2015 |
Michael AffenzellerProfessor, University of Applied Sciences Upper AustriaEmail confirmado em fh-hagenberg.at
