Photo of Jakob Bossek

Dr. Jakob Bossek

Assistant Professor at the Department of Computer Science
Chair for AI Methodology (AIM)
 RWTH Aachen University
Theaterstrasse 35-39, 52062 Aachen, Germany
bossek[at]aim[dot]rwth-aachen[dot]de

Welcome

Welcome dear visitor! Glad to see you here. My name is Jakob Bossek. I am computer scientist. Currently, I am assistant professor (Akademischer Rat) at the Department of Computer Science, RWTH Aachen University in Aachen (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

11/04/2022

Success at GECCO'22

I have one full paper and one poster paper accepted at the annual 2022 edition of the ACM Genetic and Evolutionary Computation Conference (GECCO), a major conference in evolutionary computation.

  • Bossek, J., & Neumann, F. Exploring the Feature Space of TSP Instances Using Quality Diversity.
  • Rook, J., Trautmann, H., Bossek, J., & Grimme, C. On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems.
14/07/2021

Papers accepted at FOGA'21

In cooperation with colleagues I have the following 3 full papers accepted at the 16th ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XVI). In total 10 out of 21 submissions were accepted. FOGA is a bi-annual conference with major focus on the understanding of the search behavior of randomized search heuristics and evoltionary algorithms.

  • Bossek, J., & Sudholt, D. (2021). Do Additional Optima Speed Up Evolutionary Algorithms?
  • Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., & Kerschke, P. (2021). On the Potential of Normalized TSP Features for Automated Algorithm Selection.
  • Nikfarjam, A., Bossek, J., Neumann, A., & Neumann, F. (2021). Computing Diverse Sets of High Quality TSP Tours by EAX-Based Evolutionary Diversity Optimisation.
26/04/2021

Paper accepted at GECCO'21 ECPERM Workshop

Another paper got accepted at the Evolutionary Computation for Permutation Problems (ECPERM) workshop at this years Genetic and Evolutionary Computation Conference (GECCO). Together with my dear collegue Markus Wagner from the University of Adelaide, Australia, I studied the problem of evolving problem instances for the Traveling Thief Problem (TTP) with different performance rankings for more than two algorithms (paper title: Generating Instances with Performance Differences for More Than Just Two Algorithms).

01/04/2021

EDO Tutorial at CEC'21

Our tutorial proposal was accepted at this year IEEE Congress on Evolutionary Computation (CEC). I will give a tutorial on Evolutionary Diversity Optimization with Aneta Neumann and Frank Neumann from the School of Computer Science, The University of Adelaide, Australia. Find more details on the accompanying website.

01/04/2021

Success at GECCO'21

The annual ACM Genetic and Evolutionary Computation Conference (GECCO) is one of the major events in evolutionary computation. Together with dear collegues from the Logistics and Optimisation Group, School of Computer Science, The Univeristy of Adelaide in Australia I have 4 full papers accepted at GECCO2021. All four papers deal with Evolutionary Diversity Optimization (EDO).

  • Bossek, J., Neumann, A., & Neumann, F. Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms.
  • Bossek, J., & Neumann, F. Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem.
  • Neumann, A., Bossek, J., & Neumann, F. Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained Monotone Submodular Functions.
  • Nikfarjam, A., Bossek, J., Neumann, A., & Neumann, F. Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem.
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 doctorate degree

Received my doctorate degree 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 04/2022
Assistant Professor (Akademischer Rat) at the Department of Computer Science (Chair for AI Methodology), RWTH Aachen University, Germany
09/2020 - 03/2022
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 53 peer-reviewed publications. My h-index is 10 (according to Google Scholar). Last update: Nov 05, 2020.

Journal Aricles (peer-reviewed) [8]

  1. J. Bossek, F. Neumann, P. Peng, and D. Sudholt, “Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem,” Algorithmica, vol. 83, no. 10, pp. 3148–3179, 2021.
  2. J. Bossek, P. Kerschke, and H. Trautmann, “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms,” Applied Soft Computing, vol. 88, no. C, 2020.
  3. G. Casalicchio et al., “OpenML: An R Package to Connect to the Machine Learning Platform OpenML,” Computational Statistics, vol. 34, no. 3, pp. 977–991, 2019.
  4. J. Bossek, “Grapherator: A Modular Multi-Step Graph Generator,” Journal of Open Source Software, vol. 3, no. 22, p. 528, 2018.
  5. P. Kerschke, L. Kotthoff, J. Bossek, H. H. Hoos, and H. Trautmann, “Leveraging TSP Solver Complementarity through Machine Learning,” Evolutionary Computation, vol. 26, no. 4, pp. 597–620, 2018.
  6. 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.
  7. J. Bossek, “Smoof: Single- and Multi-Objective Optimization Test Functions,” The R Journal, vol. 9, no. 1, pp. 103–113, 2017.
  8. 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,” Annals of Mathematics and Artificial Intelligence, vol. 69, no. 2, pp. 151–182, 2013.

Conference Articles (peer-reviewed) [40]

  1. J. Bossek and F. Neumann, “Exploring the Feature Space of TSP Instances Using Quality Diversity,” in Proceedings of the Genetic and Evolutionary Computation Conference, New York, NY, USA, 2022, pp. 186–194.
  2. J. Bossek, A. Neumann, and F. Neumann, “Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion, New York, NY, USA, 2022, pp. 824–842.
  3. J. Rook, H. Trautmann, J. Bossek, and C. Grimme, “On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion, New York, NY, USA, 2022, pp. 356–359.
  4. J. Heins, J. Rook, L. Schäpermeier, P. Kerschke, J. Bossek, and H. Trautmann, “BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems,” in Parallel Problem Solving from Nature – PPSN XVII, Cham, 2022, pp. 192–206.
  5. A. Nikfarjam, A. Neumann, J. Bossek, and F. Neumann, “Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem,” in Parallel Problem Solving from Nature – PPSN XVII, Cham, 2022, pp. 237–249.
  6. J. Bossek, A. Neumann, and F. Neumann, “Exact Counting and Sampling of Optima for the Knapsack Problem,” in Learning and Intelligent Optimization: 15th International Conference, LION 15, Athens, Greece, June 20–25, 2021, Revised Selected Papers, Berlin, Heidelberg, 2021, pp. 40–54.
  7. A. Nikfarjam, J. Bossek, A. Neumann, and F. Neumann, “Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem,” in Proceedings of the Genetic and Evolutionary Computation Conference, New York, NY, USA, 2021, pp. 600–608.
  8. J. Bossek and M. Wagner, “Generating Instances with Performance Differences for More than Just Two Algorithms,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion, New York, NY, USA, 2021, pp. 1423–1432.
  9. J. Bossek, A. Neumann, and F. Neumann, “Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms,” in Proceedings of the Genetic and Evolutionary Computation Conference, New York, NY, USA, 2021, pp. 556–564.
  10. J. Bossek and F. Neumann, “Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem,” in Proceedings of the Genetic and Evolutionary Computation Conference, New York, NY, USA, 2021, pp. 198–206.
  11. A. Neumann, J. Bossek, and F. Neumann, “Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained Monotone Submodular Functions,” in Proceedings of the Genetic and Evolutionary Computation Conference, New York, NY, USA, 2021, pp. 261–269.
  12. 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, Berlin, Heidelberg, 2020, pp. 48–64.
  13. J. Bossek, C. Grimme, and H. Trautmann, “Dynamic Bi-Objective Routing of Multiple Vehicles,” in Proceedings of the 2020 Genetic and Evolutionary Computation Conference, New York, NY, USA, 2020, pp. 166–174.
  14. 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, Berlin, Heidelberg, 2020, pp. 111–124.
  15. 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, New York, NY, USA, 2020, pp. 778–786.
  16. 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.
  17. 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, pp. 1286–1294.
  18. 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, Berlin, Heidelberg, 2020, pp. 346–359.
  19. 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), Glasgow, United Kingdom, 2020, pp. 1–8.
  20. 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.
  21. 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.
  22. 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), Glasgow, United Kingdom, 2020, pp. 1–8.
  23. 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, New York, NY, USA, 2019, pp. 516–523.
  24. 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, New York, NY, USA, 2019, pp. 58–71.
  25. 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, New York, NY, USA, 2019, pp. 1443–1451.
  26. J. Bossek and C. Grimme, “Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria Shortest Path Problems,” in Learning and Intelligent Optimization, Cham, 2019, pp. 184–198.
  27. 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, New York, NY, USA, 2019, pp. 102–115.
  28. J. Bossek, C. Grimme, S. Meisel, G. Rudolph, and H. Trautmann, “Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm,” in Evolutionary Multi-Criterion Optimization, Cham, 2019, pp. 516–528.
  29. J. Bossek and H. Trautmann, “Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time,” in Learning and Intelligent Optimization, Cham, 2019, pp. 215–219.
  30. 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, New York, NY, USA, 2018, pp. 1737–1744.
  31. 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, New York, NY, USA, 2018, pp. 585–592.
  32. J. Bossek, “Performance Assessment of Multi-Objective Evolutionary Algorithms with the R Package Ecr,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion, New York, NY, USA, 2018, pp. 1350–1356.
  33. J. Bossek, “Ecr 2.0: A Modular Framework for Evolutionary Computation in R,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion, New York, NY, USA, 2017, pp. 1187–1193.
  34. 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), 2017, pp. 1–8.
  35. 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), 2017, pp. 1–8.
  36. J. Bossek and H. Trautmann, “Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference,” in Proceedings of the XV International Conference of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037, Berlin, Heidelberg, 2016, pp. 3–12.
  37. J. Bossek and H. Trautmann, “Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers,” in Learning and Intelligent Optimization, Cham, 2016, pp. 48–59.
  38. 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 2015 Annual Conference on Genetic and Evolutionary Computation, New York, NY, USA, 2015, pp. 425–432.
  39. 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 2015 Annual Conference on Genetic and Evolutionary Computation, New York, NY, USA, 2015, pp. 1319–1326.
  40. 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 Revised Selected Papers of the 6th International Conference on Learning and Intelligent Optimization - Volume 7219, Berlin, Heidelberg, 2012, pp. 115–129.

Books [1]

  1. C. Grimme and J. Bossek, Einführung in Die Optimierung — Konzepte, Methoden Und Anwendungen, First. Springer Vieweg, 2018.

Technical Reports & Preprints [1]

  1. 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, 2017.

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. During the corona pandemic I started to play the drums.

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.