Jakob Bossek
TitleCited byYear
mlr: Machine Learning in R
B Bischl, M Lang, L Kotthoff, J Schiffner, J Richter, E Studerus, ...
The Journal of Machine Learning Research 17 (1), 5938-5942, 2016
2312016
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
512013
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
412017
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
272012
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
172017
smoof: Single-and multi-objective optimization test functions
J Bossek
The R Journal 9 (1), 103-113, 2017
152017
mlr: Machine Learning in R. R package version 2.9
B Bischl, M Lang, J Richter, J Bossek, L Judt, T Kuehn, E Studerus, ...
122015
ecr 2.0: a modular framework for evolutionary computation in R
J Bossek
Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2017
112017
Leveraging TSP solver complementarity through machine learning
P Kerschke, L Kotthoff, J Bossek, HH Hoos, H Trautmann
Evolutionary computation 26 (4), 597-620, 2018
82018
mlrmbo: Model-based optimization for mlr
B Bischl, J Bossek, D Horn, M Lang
R package version 1, 92-07, 2015
82015
smoof: Single and Multi-Objective Optimization Test Functions (2016)
J Bossek
R package version 1, 9000, 0
8
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
52015
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
52015
Nichtlineare Optimierung
C Grimme, J Bossek
Einführung in die Optimierung, 149-189, 2018
42018
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
42017
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
32018
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
32016
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
22018
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
22018
mcMST: A Toolbox for the Multi-Criteria Minimum Spanning Tree Problem.
J Bossek
J. Open Source Software 2 (17), 374, 2017
22017
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