Jakob Bossek

Dr Jakob Bossek

Researcher

School of Computer Science

Faculty of Engineering, Computer and Mathematical Sciences

Eligible to supervise Masters and PhD - email supervisor to discuss availability.


Jakob Bossek received his bachelor degree in Statistics and diploma in Computer Science from the TU Dortmund University (Germany) in 2013 and 2014, respectively. In 2018 he finished his PhD in Information Systems at the University of Münster, Germany. He now is a researcher at the School of Computer Science at the University of Adelaide.

His broad research topics include practical and theoretical aspects of bio-inspired problem solving, in particular evolutionary algorithms for NP-hard combinatorial optimization problems.

  • Bioinspired Algorithms
  • Theory of randomized search heuristics
  • (Evolutionary) Multi-Objective Optimization
  • Combinatorial Optimization
  • Black-Box Optimization
  • Algorithm Selection

 

    Expand
  • Journals

    Year Citation
    2019 Bossek, J., Kerschke, P., & Trautmann, H. (2019). A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms. Applied Soft Computing.
    DOI
    2018 Kerschke, P., Kotthoff, L., Bossek, J., Hoos, H., & Trautmann, H. (2018). Leveraging TSP solver complementarity through machine learning. Evolutionary Computation, 26(4), 597-620.
    DOI Scopus7 WoS2
    2018 Bossek, J. (2018). grapherator: A Modular Multi-Step Graph Generator. The Journal of Open Source Software, 3(22), 528.
    DOI
    2017 Bossek, J. (2017). mcMST: A Toolbox for the Multi-Criteria Minimum Spanning Tree Problem. The Journal of Open Source Software, 2(17), 1-2.
    DOI
    2017 Casalicchio, G., Bossek, J., Lang, M., Kirchhoff, D., Kerschke, P., Hofner, B., . . . Bischl, B. (2017). OpenML: An R Package to Connect to the Networked Machine Learning Platform OpenML. Computational Statistics, 32, 1-15.
    DOI
    2017 Bossek, J. (2017). smoof: Single- and multi-objective optimization test functions. R Journal, 9(1), 103-113.
    DOI Scopus9 WoS3
    2013 Mersmann, O., Bischl, B., Trautmann, H., Wagner, M., Bossek, J., & Neumann, F. (2013). A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem. Annals of Mathematics and Artificial Intelligence, 69(2), 151-182.
    DOI Scopus41 WoS22
    Bischl, B., Richter, J., Bossek, J., Horn, D., Thomas, J., & Lang, M. (n.d.). mlrMBO: A Modular Framework for Model-Based Optimization of Expensive
    Black-Box Functions.
  • Conference Papers

    Year Citation
    2019 Bossek, J., Grimme, C., Meisel, S., Rudolph, G., & Trautmann, H. (2019). Bi-objective orienteering: Towards a dynamic multi-objective evolutionary algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11411 LNCS (pp. 516-528). Switzerland: Springer.
    DOI
    2019 Bossek, J., & Trautmann, H. (2019). Multi-objective performance measurement: Alternatives to PAR10 and expected running time. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11353 LNCS (pp. 215-219).
    DOI Scopus1
    2019 Bossek, J., & Grimme, C. (2019). Solving scalarized subproblems within evolutionary algorithms for multi-criteria shortest path problems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11353 LNCS (pp. 184-198).
    DOI
    2019 Bossek, J., Kerschke, P., Neumann, A., Wagner, M., Neumann, F., & Trautmann, H. (2019). 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. Germany: ACM.
    2019 Bossek, J., & Sudholt, D. (2019). 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. Potsdam: ACM.
    2019 Bossek, J., Grimme, C., & Neumann, F. (2019). On the benefits of biased edge-exchange mutation for the multi-criteria spanning tree problem. In GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference (pp. 516-523). online: ACM.
    DOI
    2019 Bossek, J., Neumann, F., Peng, P., & Sudholt, D. (2019). Runtime analysis of randomized search heuristics for dynamic graph coloring. In GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference (pp. 1443-1451). online: ACM.
    DOI
    2018 Bossek, J. (2018). Performance assessment of multi-objective evolutionary algorithms with the R package ecr. In H. E. Aguirre, & K. Takadama (Eds.), GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion (pp. 1350-1356). Online: ACM.
    DOI Scopus1
    2018 Kerschke, P., Bossek, J., & Trautmann, H. (2018). Parameterization of state-of-the-art performance indicators: A robustness study based on inexact TSP solvers. In H. E. Aguirre, & K. Takadama (Eds.), GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion (pp. 1737-1744). Online: ACM.
    DOI Scopus2
    2018 Bossek, J., Grimme, C., Meisel, S., Rudolph, G., & Trautmann, H. (2018). Local search eects in Bi-objective orienteering. In H. E. Aguirre, & K. Takadama (Eds.), GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference (pp. 585-592). Online: ACM.
    DOI Scopus2
    2018 Bossek, J., & Grimme, C. (2018). A pareto-beneficial sub-tree mutation for the multi-criteria minimum spanning tree problem. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings Vol. 2018-January (pp. 1-8). Online: IEEE.
    DOI Scopus3
    2018 Bossek, J., & Grimme, C. (2018). An extended mutation-based priority-rule integration concept for multi-objective machine scheduling. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings Vol. 2018-January (pp. 1-8). Online: IEEE.
    DOI Scopus2
    2017 Bossek, J. (2017). Ecr 2.0: A modular framework for evolutionary computation in R. In P. A. Bosman (Ed.), GECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1187-1193). ACM.
    DOI Scopus9
    2016 Bossek, J., & Trautmann, H. (2016). Evolving instances for maximizing performance differences of state-of-the-art inexact TSP solvers. In P. Festa, M. Sellmann, & J. Vanschoren (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10079 LNCS (pp. 48-59). Online: SPRINGER INTERNATIONAL PUBLISHING AG.
    DOI WoS1
    2016 Bossek, J., & Trautmann, H. (2016). Understanding characteristics of evolved instances for state-of-the-art inexact TSP solvers with maximum performance difference. In G. Adorni, S. Cagnoni, M. Gori, & M. Maratea (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10037 LNAI (pp. 3-12). Online: SPRINGER INTERNATIONAL PUBLISHING AG.
    DOI Scopus3 WoS2
    2015 Bossek, J., Bischl, B., Rudolph, G., & Wagner, T. (2015). Learning feature-parameter mappings for parameter tuning via the profile expected improvement. In S. Silva (Ed.), GECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conference (pp. 1319-1326). Online: ASSOC COMPUTING MACHINERY.
    DOI Scopus3 WoS2
    2015 Meisel, S., Grimme, C., Bossek, J., Wölck, M., Rudolph, G., & Trautmann, H. (2015). Evaluation of a multi-objective EA on benchmark instances for dynamic routing of a vehicle. In S. Silva (Ed.), GECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conference (pp. 425-432). Online: ASSOC COMPUTING MACHINERY.
    DOI Scopus5 WoS3
    2012 Mersmann, O., Bischl, B., Bossek, J., Trautmann, H., Wagner, M., & Neumann, F. (2012). Local search and the traveling salesman problem: A feature-based characterization of problem hardness. In Proceedings of the 6th International Conference on Learning and Intelligent Optimization, LION 6 Vol. 7219 LNCS (pp. 115-129). Germany: Springer-Verlag.
    DOI Scopus20
  • Position: Researcher
  • Email: jakob.bossek@adelaide.edu.au
  • Campus: North Terrace
  • Building: Ingkarni Wardli, floor 4
  • Room: 4 46
  • Org Unit: School of Computer Science

Connect With Me
External Profiles