• 2020

    Aufsatz (Zeitschrift)

    Bossek, J., Kerschke, P., & Trautmann, H. (2020). A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms. Applied Soft Computing, 2020(88), 105901.
    Mehr Details BibTeX Gesamter Text DOI

    Aufsatz (Konferenz)

    Anh, D. V., Bossek, J., Neumann, A., & Neumann, F. (2020). Evolving Diverse Sets of Tours for the Travelling Salesperson Problem. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '20), Cancun, Mexico. (Accepted)
    Mehr Details BibTeX

    Bossek, J., Neumann, A., & Neumann, F. (2020). Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions. In Proceedings of the Parallel Problem Solving from Nature (PPSN XVI), Leiden. (Accepted)
    Mehr Details BibTeX

    Bossek, J., Casel, K., Kerschke, P., & Neumann, F. (2020). The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '20), Cancun, Mexico. (Accepted)
    Mehr Details BibTeX Gesamter Text DOI

    Bossek, J., Doerr, C., & Kerschke, P. (2020). Initial Design Strategies and their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '20), Cancun, Mexico. (Accepted)
    Mehr Details BibTeX DOI

    Bossek, J., Doerr, C., Kerschke, P., Neumann, A., & Neumann, F. (2020). Evolving Sampling Strategies for One-Shot Optimization Tasks. In Proceedings of the 16th International Conference on Parallel Problem Solving from Nature (PPSN XVI), Leiden, The Netherlands. (Accepted)
    Mehr Details BibTeX

    Bossek, J., Grimme, C., Rudolph, G., & Trautmann, H. (2020). Towards Decision Support in Dynamic Bi-Objective Vehicle Routing. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Glasgow, UK. (Accepted)
    Mehr Details BibTeX

    Bossek, J., Grimme, C., & Trautmann, H. (2020). Dynamic Bi-Objective Routing of Multiple Vehicles. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '20), Cancun, Mexico. (Accepted)
    Mehr Details BibTeX

    Bossek, J., Kerschke, P., & Trautmann, H. (2020). Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Glasgow, UK. (Accepted)
    Mehr Details BibTeX Gesamter Text

    Bossek, J., Neumann, F., Peng, P., & Sudholt, D. (2020). More Effective Evolutionary Algorithms for Graph Coloring Through Dynamic Optimization. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '20), Cancun, Mexico. (Accepted)
    Mehr Details BibTeX

    Roostapour, V., Bossek, J., & Neumann, F. (2020). Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '20), Cancun, Mexico. (Accepted)
    Mehr Details BibTeX

    Seiler, M. V., Pohl, J., Bossek, J., Kerschke, P., & Trautmann, H. (2020). Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. In Proceedings of the 16th International Conference on Parallel Problem Solving from Nature (PPSN XVI), Leiden, The Netherlands. (Accepted)
    Mehr Details BibTeX

  • 2019

    Aufsatz (Zeitschrift)

    Casalicchio, G., Bossek, J., Lang, M., Kirchhoff, D., Kerschke, P., Hofner, B., Seibold, H., Vanschoren, J., & Bischl, B. (2019). OpenML: An R package to connect to the machine learning platform OpenML. Computational Statistics, 2019, 977–991.
    Mehr Details BibTeX Gesamter Text DOI

    Aufsatz (Konferenz)

    Bossek, J., & Grimme, C. (2019). Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-Criteria Shortest Path Problems. In Battiti, R., Brunato, M., Kotsireas, I., & Pardalos, P. (Eds.), Learning and Intelligent Optimization (pp. 184–198). Lecture Notes in Computer Science: Vol. 11353. Cham: Springer.
    Mehr Details BibTeX DOI

    Bossek, J., Grimme, C., & Neumann, F. (2019). 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 '19), Prague, Czech Republic, 516–523.
    Mehr Details BibTeX

    Bossek, J., Neumann, F., Peng, P., & Sudholt, D. (2019). Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '19), Prague, Czech Republic, 1443–1451.
    Mehr Details BibTeX DOI

    Bossek, J., & Trautmann, H. (2019). Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time. In Battiti, R., Brunato, M., Kotsireas, I., & Pardalos, P. (Eds.), Learning and Intelligent Optimization (pp. 215–219). Lecture Notes in Computer Science: Vol. 11353. Cham: Springer.
    Mehr Details BibTeX

    Bossek, J., Grimme, C., Meisel, S., Rudolph, G., & Trautmann, H. (2019). Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm. In Deb, K., Goodman, E., Coello, C. C. A., Klamroth, K., Miettinen, K., Mostaghim, S., & Reed, P. (Eds.), Evolutionary Multi-Criterion Optimization (EMO) (pp. 516–528). Lecture Notes in Computer Science: Vol. 11411. Springer International Publishing.
    Mehr Details BibTeX DOI

    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 Workshop on Foundations of Genetic Algorithms (FOGA XV), Potsdam, Germany, 58–71.
    Mehr Details BibTeX DOI

    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 Workshop on Foundations of Genetic Algorithms (FOGA XV), Potsdam, Germany, 102–115.
    Mehr Details BibTeX DOI

    Kurzbeitrag (Poster)

    Bossek, J. (2019). Evolutionary Computation in R with the ecr Package'. Poster session presented at the useR! 2019, Toulouse, France. (Accepted)
    Mehr Details BibTeX

  • 2018

    Aufsatz (Zeitschrift)

    Bossek, J. (2018). grapherator: A Modular Multi-Step Graph Generator. The Journal of Open Source Software, 2018.
    Mehr Details BibTeX

    Kerschke, P., Kotthoff, L., Bossek, J., Hoos, H. H., & Trautmann, H. (2018). Leveraging TSP Solver Complementarity through Machine Learning. Evolutionary Computation (ECJ), 26(4), 597–620.
    Mehr Details BibTeX Gesamter Text DOI

    Aufsatz (Konferenz)

    Bossek, J. (2018). Performance Assessment of Multi-Objective Evolutionary Algorithms With the R Package ecr. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '18) Companion, Kyoto, Japan, 1350–1356.
    Mehr Details BibTeX DOI

    Bossek, J., Grimme, C., Meisel, S., Rudolph, G., & Trautmann, H. (2018). Local Search Effects in Bi-Objective Orienteering. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '18), Kyoto, Japan, 585–592.
    Mehr Details BibTeX DOI

    Kerschke, P., Bossek, J., & Trautmann, H. (2018). 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 (GECCO '18) Companion, Kyoto, Japan, 1737–1744.
    Mehr Details BibTeX Gesamter Text DOI

    Buch (Monographie)

    Grimme, C., & Bossek, J. (2018). Einführung in die Optimierung — Konzepte, Methoden und Anwendungen (1st ed.). Springer Vieweg.
    Mehr Details BibTeX DOI

    Abschlussarbeit (Dissertation, Habilitation)

    Bossek, J. (2018). Investigating Problem Hardness in (Multi-Objective) Combinatorial Optimization: Algorithm Selection, Instance Generation and Tailored Algorithm Design. Dissertation at the Universität Münster. (In press)
    Mehr Details BibTeX

  • 2017

    Aufsatz (Zeitschrift)

    Bossek, J. (2017). mcMST: A Toolbox for the Multi-Criteria Minimum Spanning Tree Problem. The Journal of Open Source Software, 2017.
    Mehr Details BibTeX DOI

    Bossek, J. (2017). smoof: Single- and Multi-Objective Optimization Test Functions. The R Journal, 2017(1), 103–113.
    Mehr Details BibTeX Gesamter Text

    Aufsatz (Konferenz)

    Bossek, J., & Grimme, C. (2017). An Extended Mutation-Based Priority-Rule Integration Concept for Multi-Objective Machine Scheduling. In Proceedings of the IEEE Symposium Series on Computational Intelligence, Honolulu, Hawaii.
    Mehr Details BibTeX DOI

    Bossek, J., & Grimme, C. (2017). A Pareto-Beneficial Sub-Tree Mutation for the Multi-Criteria Minimum Spanning Tree Problem. In Proceedings of the IEEE Symposium Series on Computational Intelligence, Honolulu, Hawai, 3280–3287.
    Mehr Details BibTeX DOI

    Bossek, J. (2017). ecr 2.0: A Modular Framework for Evolutionary Computation in R. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '17) Companion, Berlin, Germany.
    Mehr Details BibTeX DOI

    Sonstige (technische Spezifikation, informelle Veröffentlichung)

    Bischl, B., Richter, J., Bossek, J., Horn, D., Thomas, J., & Lang, M. (2017). mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions.
    Mehr Details BibTeX Gesamter Text

    Casalicchio, G., Bossek, J., Lang, M., Kirchhoff, D., Kerschke, P., Hofner, B., Seibold, H., Vanschoren, J., & Bischl, B. (2017). OpenML: An R Package to Connect to the Networked Machine Learning Platform OpenML.
    Mehr Details BibTeX Gesamter Text DOI

  • 2016

    Aufsatz (Konferenz)

    Bossek, J., & Trautmann, H. (2016). Evolving Instances for Maximizing Performance Differences of State-of-The-Art Inexact TSP Solvers. In Festa, P., Sellmann, M., & Vanschoren, J. (Eds.), Learning and Intelligent Optimization (pp. 48–59). Lecture Notes in Computer Science: Vol. 10079. Springer International Publishing.
    Mehr Details BibTeX DOI

    Bossek, J., & Trautmann, H. (2016). Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference. In Adorni, G., Cagnoni, S., Gori, M., & Maratea, M. (Eds.), AI*IA 2016 Advances in Artificial Intelligence (pp. 3–12). Lecture Notes in Computer Science: Vol. 10037. Cham: Springer.
    Mehr Details BibTeX DOI

  • 2015

    Aufsatz (Konferenz)

    Bossek, J., Bischl, B., Wagner, T., & Rudolph, G. (2015). Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '15), Madrid, Spanien.
    Mehr Details BibTeX

    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 Proceedings of the Genetic and Evolutionary Computation Conference, Madrid, Spain, 425–432.
    Mehr Details BibTeX DOI

  • 2013

    Aufsatz (Zeitschrift)

    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 (Annals of Mathematics and Artificial Intelligence), 69(2), 151–182.
    Mehr Details BibTeX DOI

  • 2012

    Aufsatz (Zeitschrift)

    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. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)), 7219 LNCS, 115–129.
    Mehr Details BibTeX DOI