Jakob Bossek
Cited by
Cited by
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
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
smoof: Single-and Multi-Objective Optimization Test Functions.
J Bossek
R J. 9 (1), 103, 2017
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 (3), 977-991, 2019
Leveraging TSP solver complementarity through machine learning
P Kerschke, L Kotthoff, J Bossek, HH Hoos, H Trautmann
Evolutionary computation 26 (4), 597-620, 2018
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
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
ecr 2.0: a modular framework for evolutionary computation in R
J Bossek
Proceedings of the genetic and evolutionary computation conference companion …, 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
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
Einführung in die Optimierung
C Grimme, J Bossek
Springer Fachmedien Wiesbaden, 2018
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
BBmisc: Miscellaneous Helper Functions for B
B Bischl, M Lang, J Bossek, D Horn, J Richter, D Surmann
Bischl. R package version 1, 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, ...
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
BBmisc: miscellaneous helper functions for B. Bischl
B Bischl, M Lang, J Bossek, D Horn, J Richter, D Surmann
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
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
A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms
J Bossek, P Kerschke, H Trautmann
Applied Soft Computing 88, 105901, 2020
mlrMBO: model-based optimization for mlr
B Bischl, J Bossek, D Horn, M Lang
R package version 1, 92-07, 2015
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