• 2021

    Research article in proceedings (conference)

    Bossek, J., Neumann, A., & Neumann, F. (2021). Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '21), Lille, France. (accepted / in press (not yet published))
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    Bossek, J., Neumann, A., & Neumann, F. (2021). Exact Counting and Sampling of Optima for the Knapsack Problem. In Proceedings of the Learning and Intelligent Optimization, Athens, Greece. (accepted / in press (not yet published))
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    Bossek, J., & Neumann, F. (2021). Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '21), Lille, France. (accepted / in press (not yet published))
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    Bossek, J., & Sudholt, D. (2021). Do Additional Optima Speed Up Evolutionary Algorithms?. In Proceedings of the 16th ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XVI), Dornbirn, Austria. (accepted / in press (not yet published))
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    Bossek, J., & Wagner, M. (2021). Generating Instances with Performance Differences for More Than Just Two Algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '21), Lille, France. (accepted / in press (not yet published))
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    Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., & Kerschke, P. (2021). On the Potential of Normalized TSP Features for Automated Algorithm Selection. In Association, f. C. M. (Ed.), Proceedings of the 16th ACM/SIGEVO Conference on Foundations of genetic Algorithms (FOGA XVI) (pp. 1–15). Dornbirn, Austria: ACM Press.
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    Neumann, A., Bossek, J., & Neumann, F. (2021). Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained Monotone Submodular Functions. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '21), Lille, France. (accepted / in press (not yet published))
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    Nikfarjam, A., Bossek, J., Neumann, A., & Neumann, F. (2021). Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '21), Lille, France. (accepted / in press (not yet published))
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    Nikfarjam, A., Bossek, J., Neumann, A., & Neumann, F. (2021). Computing Diverse Sets of High Quality TSP Tours by EAX-Based Evolutionary Diversity Optimisation. In Proceedings of the 16th ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XVI), Dornbirn, Austria. (accepted / in press (not yet published))
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    Research article (journal)

    Bossek, J., Peng, P., Neumann, F., & Sudholt, D. (2021). Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem. Algorithmica, 2021. (accepted / in press (not yet published))
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  • 2020

    Research article in proceedings (conference)

    Anh, D. V., Bossek, J., Neumann, A., & Neumann, F. (2020). Evolving Diverse Sets of Tours for the Travelling Salesperson Problem. In Carlos, A. C. (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '20) (pp. 681–689). New York: ACM.
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    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 / in press (not yet published))
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    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, 1286–1294.
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    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, 778–786.
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    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, 111–124.
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    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, 1–8.
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    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, 166–174.
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    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, 1–8.
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    Bossek, J., Neumann, F., Peng, P., & Sudholt, D. (2020). More Effective Evolutionary Algorithms for Graph Coloring Through Dynamic Optimization. In Carlos, A. C. (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '20) (pp. 1277–1285). New York: ACM.
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    Roostapour, V., Bossek, J., & Neumann, F. (2020). Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem. In Carlos, A. C. (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '20) (pp. 551–559). New York: ACM.
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    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, 48–64.
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    Research article (journal)

    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.
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    Other scientific publication

    Bartz-Beielstein, T., Doerr, C., Bossek, J., Chandrasekaran, S., Eftimov, T., Fischbach, A., Kerschke, P., López-Ibáñez, M., Malan, K. M., Moore, J. H., Naujoks, B., Orzechowski, P., Volz, V., Wagner, M., & Weise, T. (2020). Benchmarking in Optimization: Best Practice and Open Issues.
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    Bossek, J., & Neumann, F. (2020). Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem.
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    Neumann, A., Bossek, J., & Neumann, F. (2020). Computing Diverse Sets of Solutions for Monotone Submodular Optimisation Problems.
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  • 2019

    Research article in proceedings (conference)

    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.
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    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.
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    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.
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    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.
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    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.
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    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.
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    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.
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    Abstract in Online-Sammlung (Konferenz)

    Bossek, J. (2019). Evolutionary Computation in R with the ecr Package'. Poster session presented at the useR! 2019, Toulouse, France. (accepted / in press (not yet published))
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    Research article (journal)

    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.
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    Other scientific publication

    Bossek, J., Kerschke, P., Neumann, A., Neumann, F., & Doerr, C. (2019). One-Shot Decision-Making with and without Surrogates.
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  • 2018

    Book (monograph)

    Grimme, C., & Bossek, J. (2018). Einführung in die Optimierung — Konzepte, Methoden und Anwendungen (1st ed.). Springer Vieweg.
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    Research article in proceedings (conference)

    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.
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    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.
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    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.
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    Research article (journal)

    Bossek, J. (2018). grapherator: A Modular Multi-Step Graph Generator. The Journal of Open Source Software, 2018.
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    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.
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    Thesis (doctoral or post-doctoral)

    Bossek, J. (2018). Investigating Problem Hardness in (Multi-Objective) Combinatorial Optimization: Algorithm Selection, Instance Generation and Tailored Algorithm Design. at the Universität Münster. (online first)
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  • 2017

    Research article in proceedings (conference)

    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.
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    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.
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    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.
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    Research article (journal)

    Bossek, J. (2017). mcMST: A Toolbox for the Multi-Criteria Minimum Spanning Tree Problem. The Journal of Open Source Software, 2017.
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    Bossek, J. (2017). smoof: Single- and Multi-Objective Optimization Test Functions. The R Journal, 2017(1), 103–113.
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    Other scientific publication

    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.
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    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.
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  • 2016

    Research article in proceedings (conference)

    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.
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    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.
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  • 2015

    Research article in proceedings (conference)

    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.
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    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.
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  • 2013

    Research article (journal)

    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), 182.
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  • 2012

    Research article (journal)

    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) (LNCS), 7219 LNCS, 129.
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