| 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 | 183 | 2017 |
| Benchmarking in optimization: Best practice and open issues T Bartz-Beielstein, C Doerr, D Berg, J Bossek, S Chandrasekaran, ... arXiv preprint arXiv:2007.03488, 2020 | 115 | 2020 |
| 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, 151-182, 2013 | 98 | 2013 |
| Leveraging TSP solver complementarity through machine learning P Kerschke, L Kotthoff, J Bossek, HH Hoos, H Trautmann Evolutionary computation 26 (4), 597-620, 2018 | 86 | 2018 |
| 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 34, 977-991, 2019 | 67 | 2019 |
| smoof: Single-and Multi-Objective Optimization Test Functions. J Bossek R J. 9 (1), 103, 2017 | 59 | 2017 |
| Evolving diverse TSP instances by means of novel and creative mutation operators J Bossek, P Kerschke, A Neumann, M Wagner, F Neumann, H Trautmann Proceedings of the 15th ACM/SIGEVO conference on foundations of genetic …, 2019 | 56 | 2019 |
| 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 Learning and Intelligent Optimization: 6th International Conference, LION 6 …, 2012 | 51 | 2012 |
| Initial design strategies and their effects on sequential model-based optimization: an exploratory case study based on BBOB J Bossek, C Doerr, P Kerschke Proceedings of the 2020 genetic and evolutionary computation conference, 778-786, 2020 | 34 | 2020 |
| ecr 2.0: a modular framework for evolutionary computation in R J Bossek Proceedings of the genetic and evolutionary computation conference companion …, 2017 | 30 | 2017 |
| Einführung in die Optimierung C Grimme, J Bossek Springer Fachmedien Wiesbaden, 2018 | 29 | 2018 |
| BBmisc: Miscellaneous helper functions for B. Bischl B Bischl, M Lang, J Bossek, D Horn, J Richter, D Surmann R package version 1.11, 2017 | 26 | 2017 |
| Deep learning as a competitive feature-free approach for automated algorithm selection on the traveling salesperson problem M Seiler, J Pohl, J Bossek, P Kerschke, H Trautmann International Conference on Parallel Problem Solving from Nature, 48-64, 2020 | 25 | 2020 |
| BBmisc: Miscellaneous helper functions for B B Bischl, M Lang, J Bossek, D Horn, J Richter, D Surmann Bischl. R package version 1, 2017, 2017 | 25 | 2017 |
| Evolving diverse sets of tours for the travelling salesperson problem AV Do, J Bossek, A Neumann, F Neumann Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 681-689, 2020 | 23 | 2020 |
| Diversifying greedy sampling and evolutionary diversity optimisation for constrained monotone submodular functions A Neumann, J Bossek, F Neumann Proceedings of the Genetic and Evolutionary Computation Conference, 261-269, 2021 | 21 | 2021 |
| Evolutionary diversity optimization and the minimum spanning tree problem J Bossek, F Neumann Proceedings of the Genetic and Evolutionary Computation Conference, 198-206, 2021 | 19 | 2021 |
| Entropy-based evolutionary diversity optimisation for the traveling salesperson problem A Nikfarjam, J Bossek, A Neumann, F Neumann Proceedings of the Genetic and Evolutionary Computation Conference, 600-608, 2021 | 18 | 2021 |
| mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions, 2017 B Bischl, J Richter, J Bossek, D Horn, J Thomas, M Lang URL http://arxiv. org/abs/1703 3373, 3, 2016 | 18 | 2016 |
| Runtime analysis of randomized search heuristics for dynamic graph coloring J Bossek, F Neumann, P Peng, D Sudholt Proceedings of the Genetic and Evolutionary Computation Conference, 1443-1451, 2019 | 17 | 2019 |
Heike TrautmannProfessor of Machine Learning and Optimisation, Paderborn University, Germany在 uni-paderborn.de 的电子邮件经过验证
