| A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem O Mersmann, B Bischl, H Trautmann, M Wagner, J Bossek, F Neumann Annals of Mathematics and Artificial Intelligence 69 (2), 151-182, 2013 | 56 | 2013 |
| mlrMBO: A modular framework for model-based optimization of expensive black-box functions B Bischl, J Richter, J Bossek, D Horn, J Thomas, M Lang arXiv preprint arXiv:1703.03373, 2017 | 55 | 2017 |
| Local search and the traveling salesman problem: A feature-based characterization of problem hardness O Mersmann, B Bischl, J Bossek, H Trautmann, M Wagner, F Neumann International Conference on Learning and Intelligent Optimization, 115-129, 2012 | 30 | 2012 |
| OpenML: An R package to connect to the machine learning platform OpenML G Casalicchio, J Bossek, M Lang, D Kirchhoff, P Kerschke, B Hofner, ... Computational Statistics, 1-15, 2017 | 22 | 2017 |
| smoof: Single-and multi-objective optimization test functions J Bossek The R Journal 9 (1), 103-113, 2017 | 17 | 2017 |
| mlr: Machine Learning in R. R package version 2.9 B Bischl, M Lang, J Richter, J Bossek, L Judt, T Kuehn, E Studerus, ... | 12 | 2015 |
| Leveraging TSP solver complementarity through machine learning P Kerschke, L Kotthoff, J Bossek, HH Hoos, H Trautmann Evolutionary computation 26 (4), 597-620, 2018 | 11 | 2018 |
| ecr 2.0: a modular framework for evolutionary computation in R J Bossek Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2017 | 11 | 2017 |
| mlrmbo: Model-based optimization for mlr B Bischl, J Bossek, D Horn, M Lang R package version 1, 92-07, 2015 | 8 | 2015 |
| smoof: Single and Multi-Objective Optimization Test Functions (2016) J Bossek R package version 1, 9000, 0 | 8 | |
| Evaluation of a multi-objective EA on benchmark instances for dynamic routing of a vehicle S Meisel, C Grimme, J Bossek, M Wölck, G Rudolph, H Trautmann Proceedings of the 2015 Annual Conference on Genetic and Evolutionary …, 2015 | 7 | 2015 |
| Multi-objective performance measurement: Alternatives to PAR10 and expected running time J Bossek, H Trautmann International Conference on Learning and Intelligent Optimization, 215-219, 2018 | 6 | 2018 |
| Learning feature-parameter mappings for parameter tuning via the profile expected improvement J Bossek, B Bischl, T Wagner, G Rudolph Proceedings of the 2015 Annual Conference on Genetic and Evolutionary …, 2015 | 6 | 2015 |
| Parameterization of state-of-the-art performance indicators: A robustness study based on inexact TSP solvers P Kerschke, J Bossek, H Trautmann Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2018 | 5 | 2018 |
| BBmisc: Miscellaneous Helper Functions for B B Bischl, M Lang, J Bossek, D Horn, J Richter, D Surmann Bischl. R package version 1, 2017 | 5 | 2017 |
| Nichtlineare Optimierung C Grimme, J Bossek Einführung in die Optimierung, 149-189, 2018 | 4 | 2018 |
| A pareto-beneficial sub-tree mutation for the multi-criteria minimum spanning tree problem J Bossek, C Grimme 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1-8, 2017 | 4 | 2017 |
| Understanding characteristics of evolved instances for state-of-the-art inexact TSP solvers with maximum performance difference J Bossek, H Trautmann Conference of the Italian Association for Artificial Intelligence, 3-12, 2016 | 4 | 2016 |
| Einführung in die Optimierung: Konzepte, Methoden und Anwendungen C Grimme, J Bossek Springer-Verlag, 2018 | 3 | 2018 |
| Local search effects in bi-objective orienteering J Bossek, C Grimme, S Meisel, G Rudolph, H Trautmann Proceedings of the Genetic and Evolutionary Computation Conference, 585-592, 2018 | 2 | 2018 |
Bernd BischlHead of "Computational Statistics" Group, LMU MunichVerified email at stat.uni-muenchen.de
