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
962017
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
712013
smoof: Single-and Multi-Objective Optimization Test Functions.
J Bossek
R J. 9 (1), 103, 2017
392017
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
382012
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
332019
Leveraging TSP solver complementarity through machine learning
P Kerschke, L Kotthoff, J Bossek, HH Hoos, H Trautmann
Evolutionary computation 26 (4), 597-620, 2018
252018
Einführung in die Optimierung
C Grimme, J Bossek
Springer Fachmedien Wiesbaden, 2018
13*2018
Ecr 2.0: A modular framework for evolutionary computation in r
J Bossek
Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2017
122017
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
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
112015
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
102019
mlrMBO: model-based optimization for mlr
B Bischl, J Bossek, D Horn, M Lang
R package version 1, 92-07, 2015
92015
Benchmarking in optimization: Best practice and open issues
T Bartz-Beielstein, C Doerr, J Bossek, S Chandrasekaran, T Eftimov, ...
arXiv preprint arXiv:2007.03488, 2020
82020
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
82019
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
82018
smoof: Single and Multi-Objective Optimization Test Functions (2016)
J Bossek
URL: https://github. com/jakobbossek/smoof, 0
8
BBmisc: Miscellaneous Helper Functions for B
B Bischl, M Lang, J Bossek, D Horn, J Richter, D Surmann
Bischl. R package version 1, 2017
72017
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
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
62016
Das System kann den Vorgang jetzt nicht ausführen. Versuchen Sie es später erneut.
Artikel 1–20