Photo of Jakob Bossek

Jakob Bossek, PhD

PostDoc at the Department of Information Systems
Chair for Statistics and Optimization
 University of Münster, Münster, Germany
Leonardo-Campus 3, R208, 48149 Münster, Germany
bossek[at]wi[dot]uni-muenster[dot]de

Welcome

Welcome dear visitor! Glad to see you here. My name is Jakob Bossek. I am computer scientist. Currently, I am postdoc at the Department of Information Systems, University of Münster in Münster (Germany). My main research interest is on evolutionary computation methods. This website serves as my online scientific CV. Feel free to take a look around.

Newsfeed

23/10/2020

New website launched

Just finished the re-design of my website! I hope you like it. Used Sketch for macOS to create the graphic draft, hand-coded HTML, CSS and JavaSript to translate the graphic design into web formats. GitHub’s Jekyll serves as a static site generator.

23/10/2020

Major success at this years evolutionary computation conferences

In total 11 submissions were accepted at this years major evolutionary computation conferenes. More precisely: 2 papers at the Congress on Evolutionary Computation (CEC), 6 papers accepted at this years Genetic and Evolutionary Computation Conference (GECCO) and 3 papers accepted at the bi-annual Parallel Problem Solving from Nature (PPSN)!

11/12/2018

Received my PhD

Received my PhD from the University of Münster (Germany) with grade summa cum laude. The topic of my thesis was Investigating Problem Hardness in (Multi-Objective) Combinatorial Optimization: Algorithm Selection, Instance Generation and Tailored Algorithm Design.

Professional Development

Since 09/2020
PostDoc at the Department of Information Systems (Chair for Statistics and Optimization), University of Münster, Germany
10/2019 - 09/2020
PostDoc Researcher at the School of Computer Science, The University of Adelaide, Australia in the Optimisation and Logistics group of Prof. Dr. Frank Neumann
02/2015-09/2019
Research Associate (PhD student until Nov. 2018; later PostDoc) at the Department of Information Systems (Chair for Statistics and Optimization), University of Münster, Germany
09/2008-05/2013
Studying Statistics with minor Computer Science at the TU-Dortmund University (degree: B.Sc.)
09/2005-11/2014
Studying Computer Science with minor Statistics at the TU-Dortmund University (degree: Diplom; M.Sc. equivalent)

Publications

Find below a list of my publications split by journal, conference and other (technical reports etc.). I have 37 peer-reviewed publications. My h-index is 10 (according to Google Scholar). Last update: Nov 05, 2020.

Journal Aricles (peer-reviewed) [7]

  1. J. Bossek, P. Kerschke, and H. Trautmann, “A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms,” Appl. Soft Comput., vol. 88, p. 105901, 2020 [Online]. Available at: https://doi.org/10.1016/j.asoc.2019.105901
  2. G. Casalicchio et al., “OpenML: An {R} package to connect to the machine learning platform OpenML,” Computational Statistics Stat., vol. 34, no. 3, pp. 977–991, 2019 [Online]. Available at: https://doi.org/10.1007/s00180-017-0742-2
  3. J. Bossek, “grapherator: {A} Modular Multi-Step Graph Generator,” J. Open Source Softw., vol. 3, no. 22, p. 528, 2018 [Online]. Available at: https://doi.org/10.21105/joss.00528
  4. P. Kerschke, L. Kotthoff, J. Bossek, H. H. Hoos, and H. Trautmann, “Leveraging {TSP} Solver Complementarity through Machine Learning,” Evoliutionary Computation, vol. 26, no. 4, 2018 [Online]. Available at: https://doi.org/10.1162/evco_a_00215
  5. J. Bossek, “mcMST: {A} Toolbox for the Multi-Criteria Minimum Spanning Tree Problem,” Journal of Open Source Software, vol. 2, no. 17, p. 374, 2017 [Online]. Available at: https://doi.org/10.21105/joss.00374
  6. J. Bossek, “smoof: Single-and Multi-Objective Optimization Test Functions,” The R Journal, vol. 9, no. 1, pp. 103–113, 2017 [Online]. Available at: https://journal.r-project.org/archive/2017/RJ-2017-004/RJ-2017-004.pdf
  7. O. Mersmann, B. Bischl, H. Trautmann, M. Wagner, J. Bossek, and F. Neumann, “A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem,” Ann. Math. Artif. Intell., vol. 69, no. 2, pp. 151–182, 2013 [Online]. Available at: https://doi.org/10.1007/s10472-013-9341-2

Conference Articles (peer-reviewed) [29]

  1. V. Roostapour, J. Bossek, and F. Neumann, “Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem,” in Proceedings of the 2020 Genetic and Evolutionary Computation Conference, New York, NY, USA, 2020, pp. 551–559 [Online]. Available at: https://doi.org/10.1145/3377930.3390168
  2. J. Bossek, C. Doerr, P. Kerschke, A. Neumann, and F. Neumann, “Evolving Sampling Strategies for One-Shot Optimization Tasks,” in Parallel Problem Solving from Nature - {PPSN} {XVI} - 16th International Conference, {PPSN} 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part {I}, 2020, vol. 12269, pp. 111–124 [Online]. Available at: https://doi.org/10.1007/978-3-030-58112-1_8
  3. A. V. Do, J. Bossek, A. Neumann, and F. Neumann, “Evolving Diverse Sets of Tours for the Travelling Salesperson Problem,” in Proceedings of the 2020 Genetic and Evolutionary Computation Conference, New York, NY, USA, 2020, pp. 681–689 [Online]. Available at: https://doi.org/10.1145/3377930.3389844
  4. J. Bossek, C. Doerr, and P. Kerschke, “Initial design strategies and their effects on sequential model-based optimization: an exploratory case study based on {BBOB},” in Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 2020, pp. 778–786 [Online]. Available at: https://doi.org/10.1145/3377930.3390155
  5. M. Seiler, J. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem,” in Parallel Problem Solving from Nature - {PPSN} {XVI} - 16th International Conference, {PPSN} 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part {I}, 2020, vol. 12269, pp. 48–64 [Online]. Available at: https://doi.org/10.1007/978-3-030-58112-1_4
  6. J. Bossek, C. Grimme, G. Rudolph, and H. Trautmann, “Towards Decision Support in Dynamic Bi-Objective Vehicle Routing,” in 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings, 2020.
  7. J. Bossek, K. Casel, P. Kerschke, and F. Neumann, “The node weight dependent traveling salesperson problem: Approximation algorithms and randomized search heuristics,” in Proceedings of the 2020 Genetic and Evolutionary Computation Conference, New York, NY, USA, 2020, p. 12861294.
  8. J. Bossek, C. Grimme, and H. Trautmann, “Dynamic bi-objective routing of multiple vehicles,” in Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 2020, pp. 166–174 [Online]. Available at: https://doi.org/10.1145/3377930.3390146
  9. J. Bossek, A. Neumann, and F. Neumann, “Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: {A} Structural Comparison of Solutions,” in Parallel Problem Solving from Nature - {PPSN} {XVI} - 16th International Conference, {PPSN} 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part {I}, 2020, vol. 12269, pp. 346–359 [Online]. Available at: https://doi.org/10.1007/978-3-030-58112-1_24
  10. J. Bossek, P. Kerschke, and H. Trautmann, “Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection,” in 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings, 2020.
  11. J. Bossek, F. Neumann, P. Peng, and D. Sudholt, “More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization,” in Proceedings of the 2020 Genetic and Evolutionary Computation Conference, New York, NY, USA, 2020, pp. 1277–1285 [Online]. Available at: https://doi.org/10.1145/3377930.3390174
  12. J. Bossek and D. Sudholt, “Time complexity analysis of {RLS} and {(1} + 1) {EA} for the edge coloring problem,” in Proceedings of the 15th {ACM/SIGEVO} Conference on Foundations of Genetic Algorithms, {FOGA} 2019, Potsdam, Germany, August 27-29, 2019, 2019, pp. 102–115 [Online]. Available at: https://doi.org/10.1145/3299904.3340311
  13. J. Bossek, C. Grimme, and F. Neumann, “On the benefits of biased edge-exchange mutation for the multi-criteria spanning tree problem,” in Proceedings of the Genetic and Evolutionary Computation Conference, {GECCO} 2019, Prague, Czech Republic, July 13-17, 2019, 2019, pp. 516–523 [Online]. Available at: https://doi.org/10.1145/3321707.3321818
  14. J. Bossek, F. Neumann, P. Peng, and D. Sudholt, “Runtime analysis of randomized search heuristics for dynamic graph coloring,” in Proceedings of the Genetic and Evolutionary Computation Conference on - GECCO ’19, New York, New York, USA, 2019, pp. 1443–1451 [Online]. Available at: http://dl.acm.org/citation.cfm?doid=3321707.3321792
  15. J. Bossek, P. Kerschke, A. Neumann, M. Wagner, F. Neumann, and H. Trautmann, “Evolving diverse {TSP} instances by means of novel and creative mutation operators,” in Proceedings of the 15th {ACM/SIGEVO} Conference on Foundations of Genetic Algorithms, {FOGA} 2019, Potsdam, Germany, August 27-29, 2019, 2019, pp. 58–71 [Online]. Available at: https://doi.org/10.1145/3299904.3340307
  16. J. Bossek, C. Grimme, S. Meisel, G. Rudolph, and H. Trautmann, “Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm,” in Proceedings of the 10th International Conference on Evolutionary Multi-Criterion Optimization (EMO), East Lansing, Michigan, USA, 2019, pp. 516–528.
  17. J. Bossek, C. Grimme, S. Meisel, G. Rudolph, and H. Trautmann, “Local search effects in bi-objective orienteering,” in Proceedings of the Genetic and Evolutionary Computation Conference, {GECCO} 2018, Kyoto, Japan, July 15-19, 2018, 2018, pp. 585–592 [Online]. Available at: https://doi.org/10.1145/3205455.3205548
  18. P. Kerschke, J. Bossek, and H. Trautmann, “Parameterization of state-of-the-art performance indicators: a robustness study based on inexact {TSP} solvers,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion, {GECCO} 2018, Kyoto, Japan, July 15-19, 2018, 2018, pp. 1737–1744 [Online]. Available at: https://doi.org/10.1145/3205651.3208233
  19. J. Bossek and C. Grimme, “Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria Shortest Path Problems,” in Learning and Intelligent Optimization - 12th International Conference, {LION} 12, Kalamata, Greece, June 10-15, 2018, Revised Selected Papers, 2018, vol. 11353, pp. 184–198 [Online]. Available at: https://doi.org/10.1007/978-3-030-05348-2_17
  20. J. Bossek, “Performance assessment of multi-objective evolutionary algorithms with the {R} package ecr,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion, {GECCO} 2018, Kyoto, Japan, July 15-19, 2018, 2018, pp. 1350–1356 [Online]. Available at: https://doi.org/10.1145/3205651.3208312
  21. J. Bossek and H. Trautmann, “Multi-objective Performance Measurement: Alternatives to {PAR10} and Expected Running Time,” in Learning and Intelligent Optimization - 12th International Conference, {LION} 12, Kalamata, Greece, June 10-15, 2018, Revised Selected Papers, 2018, vol. 11353, pp. 215–219 [Online]. Available at: https://doi.org/10.1007/978-3-030-05348-2_19
  22. J. Bossek and C. Grimme, “A Pareto-Beneficial Sub-Tree Mutation for the Multi-Criteria Minimum Spanning Tree Problem,” in 2017 IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI, USA, 2017, pp. 3280–3287.
  23. J. Bossek, “Ecr 2.0: A Modular Framework for Evolutionary Computation in R,” in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) Companion, Berlin, Germany, 2017, pp. 1187–1193 [Online]. Available at: http://doi.acm.org/10.1145/3067695.3082470
  24. J. Bossek and C. Grimme, “An Extended Mutation-Based Priority-Rule Integration Concept for Multi-Objective Machine Scheduling,” in 2017 IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI, USA, 2017, pp. 3288–3295.
  25. J. Bossek and H. Trautmann, “Understanding characteristics of evolved instances for state-of-the-art inexact TSP solvers with maximum performance difference,” in AI*IA 2016 Advances in Artificial Intelligence, Genova, Italy, 2016, vol. 10037 LNAI, pp. 3–12.
  26. J. Bossek and H. Trautmann, “Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact {TSP} Solvers,” in Learning and Intelligent Optimization - 10th International Conference, {LION} 10, Ischia, Italy, May 29 - June 1, 2016, Revised Selected Papers, 2016, vol. 10079, pp. 48–59 [Online]. Available at: https://doi.org/10.1007/978-3-319-50349-3_4
  27. S. Meisel, C. Grimme, J. Bossek, M. Wölck, G. Rudolph, and H. Trautmann, “Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle,” in Proceedings of the 17th Genetic and Evolutionary Computation Conference (GECCO), New York, NY, USA, 2015, pp. 425–432.
  28. J. Bossek, B. Bischl, T. Wagner, and G. Rudolph, “Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement,” in Proceedings of the Genetic and Evolutionary Computation Conference, {GECCO} 2015, Madrid, Spain, July 11-15, 2015, 2015, pp. 1319–1326 [Online]. Available at: https://doi.org/10.1145/2739480.2754673
  29. O. Mersmann, B. Bischl, J. Bossek, H. Trautmann, M. Wagner, and F. Neumann, “Local search and the traveling salesman problem: A feature-based characterization of problem hardness,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7219 LNCS, pp. 115–129.

Books [1]

  1. C. Grimme and J. Bossek, Einführung in die Optimierung — Konzepte, Methoden und Anwendungen, 1st ed. Springer Vieweg, 2018.

Technical Reports & Preprints [4]

  1. J. Bossek and F. Neumann, “Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem,” CoRR, vol. abs/2010.1, 2020 [Online]. Available at: https://arxiv.org/abs/2010.10913
  2. A. Neumann, J. Bossek, and F. Neumann, “Computing Diverse Sets of Solutions for Monotone Submodular Optimisation Problems,” CoRR, vol. abs/2010.1, 2020 [Online]. Available at: https://arxiv.org/abs/2010.11486
  3. T. Bartz-Beielstein et al., “Benchmarking in Optimization: Best Practice and Open Issues,” CoRR, vol. abs/2007.0, 2020 [Online]. Available at: https://arxiv.org/abs/2007.03488
  4. B. Bischl, J. Richter, J. Bossek, D. Horn, J. Thomas, and M. Lang, “mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions,” CoRR, 2018 [Online]. Available at: https://arxiv.org/abs/1703.03373

Software

I am author of the R-package ecr (CRAN, GitHub repository) which is to the best of my knowledge the most comprehensive evolutionary computation toolbox in R. I am author and contributor of many more R packages. Feel free to browse my GitHub profile for a comprehensive and up to date overview.

Besides Science

Besides science I like to workout to contrast my physically less challenging office job ;-) To be more precise I do Calisthenics (ancient Greek for „the beautiful strength“), a very primal form of bodyweight training with advanced elements from classic gymnastics (e.g. static holds that give the impression as if gravity is locally disabled). Until a couple years ago I also used to to ITF Taekwon-Do (Korean: 태권도/跆拳道 [tʰɛ.k͈wʌn.do]), a form of martial arts with origins in Korea. I hold a second Dan (2nd degree black belt). At the moment though, I focus on Calisthencis only.

In addition I enjoy designing and implementing (responsive) websites from scratch. In fact this is how I first got in touch with markup and programming languages. My focus is on minimalistic, content-focused websites like this one. Unfortunately, as time is limited, I follow this passion irregularly.