Jakob Bossek

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	All	Since 2016
Citations	497	455
h-index	10	10
i10-index	12	12

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2013201420152016201720182019202020216 15 15 28 31 85 127 157 18

Co-authors

Heike TrautmannProfessor of Statistics, Department of Information Systems, University of Münster, GermanyVerified email at wi.uni-muenster.de
Frank NeumannOptimisation and Logistics, School of ComputerVerified email at adelaide.edu.au
Pascal KerschkeUniversity of MünsterVerified email at uni-muenster.de
Bernd BischlChair of Statistical Learning and Data Science, LMU MunichVerified email at stat.uni-muenchen.de
Christian GrimmeDepartment of Information Systems, University of Münster, GermanyVerified email at uni-muenster.de
Michel LangTU Dortmund; LMU MunichVerified email at statistik.tu-dortmund.de
Markus WagnerAssociate Professor of Computer Science, The University of AdelaideVerified email at adelaide.edu.au
Jakob RichterTU DortmundVerified email at statistik.tu-dortmund.de
Daniel HornStatistical Methods for Big Data, TU DortmundVerified email at tu-dortmund.de
Olaf MersmannTH KölnVerified email at p-value.net
Aneta NeumannResearcher, Research Consortium, Computer Science, AustraliaVerified email at adelaide.edu.au
Giuseppe CasalicchioPostdoctoral Researcher, LMU Munich, Munich Center for Machine LearningVerified email at campus.lmu.de
Janek ThomasFraunhofer Institute for Integrated Circuits IISVerified email at iis.fraunhofer.de
Lars KotthoffUniversity of WyomingVerified email at uwyo.edu
Stephan MeiselAssistant Professor, University of MünsterVerified email at wi.uni-muenster.de
Dirk SudholtUniversity of Passau, GermanyVerified email at uni-passau.de
Heidi SeiboldHelmholtz AI, Helmholtz Center MunichVerified email at helmholtz-muenchen.de
Joaquin VanschorenEindhoven University of TechnologyVerified email at tue.nl
Benjamin HofnerHead of Section Biostatistics, Paul-Ehrlich-InstituteVerified email at pei.de
Holger HoosLeiden University, The Netherlands & University of British Columbia, CanadaVerified email at liacs.nl

Jakob Bossek

Westfälische Wilhelms Universität Münster

Verified email at wi.uni-muenster.de - Homepage

Machine Learning Evolutionary Algorithms (Multi-Criteria) Optimization Combinatorial Optimization


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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	97	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	71	2013
smoof: Single-and Multi-Objective Optimization Test Functions. J Bossek R J. 9 (1), 103, 2017	39	2017
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	38	2012
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	34	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	26	2018
Einführung in die Optimierung C Grimme, J Bossek Springer Fachmedien Wiesbaden, 2018	15*	2018
Ecr 2.0: A modular framework for evolutionary computation in r J Bossek Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2017	12	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, ...	12	2015
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	11	2015
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	10	2020
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	10	2019
mlrMBO: model-based optimization for mlr B Bischl, J Bossek, D Horn, M Lang R package version 1, 92-07, 2015	9	2015
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	8	2019
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	8	2018
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	7	2017
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	7	2015
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	6	2018
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	6	2016

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