Jakob Bossek
Titel
Zitiert von
Zitiert von
Jahr
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
702017
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
632013
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
332012
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
282019
smoof: Single-and Multi-Objective Optimization Test Functions.
J Bossek
R J. 9 (1), 103, 2017
252017
Leveraging TSP solver complementarity through machine learning
P Kerschke, L Kotthoff, J Bossek, HH Hoos, H Trautmann
Evolutionary computation 26 (4), 597-620, 2018
172018
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
Einführung in die Optimierung
C Grimme, J Bossek
Springer Fachmedien Wiesbaden, 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
112017
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
102015
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
72015
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
62018
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
52018
BBmisc: Miscellaneous Helper Functions for B
B Bischl, M Lang, J Bossek, D Horn, J Richter, D Surmann
Bischl. R package version 1, 2017
52017
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
52017
ParamHelpers: Helpers for parameters in black-box optimization, tuning, and machine learning
B Bischl, M Lang, J Bossek, D Horn, K Schork, J Richter, P Kerschke
R package version 1, 23, 2016
52016
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
42019
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
42019
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