Jakob BossekThis is me!

Hello, I am Jakob Bossek
Welcome!

Information Systems and Statistics
University of Münster, Germany

bossek[at]wi[dot]uni-muenster[dot]de

Abstract: Hey, nice to see you! Welcome to my website. I am Jakob, postdocs at the University of Münster with a passion for good design.

I. About Me

My name is Jakob Bossek. Im am research assistant and PhD candidate at the Chair of Information Systems and Statistics at the Westfälische Wilhelms-Universität Münster. Before that, I obtained my Bachelor of Science in Statistics and Diploma in Computer Science from the Technische Universität Dortmund. I have a vast set of interests. However, my main research focus is on randomized meta-heuristics, in particular evolutionary algorithms. Here, my focus is on combinatorial multi-objective optimization and expert knowledge integration. Moreover, I am author of the R package ecr, a framework focused on rapid development of evolutionary algorithms written in the statistical programming language R.

II. News

Jakob Bossek postet at

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*.
Jakob Bossek postet at

LION 2018 conference

Currently, I am participating in the *Learning and Intelligent Optimization (LION)* conference. This years' editions takes place in the beautiful city of Kalamata in Greece I gave two talks on accepted papers.
Jakob Bossek postet at

Accompanying Website online

Currently my colleague Christian Grimme and me are polishing our upcoming book on optimization for undergraduate students. The [accompanying website is online now](http://www.optimierung-grundlagen.de).

III. Curriculum Vitae

Lastet stuff from my career.

Education

02/2015-11/2018
Doctoral Studies in Information Systems (PhD), School of Business and Economics, University of Münster
10/2006-12/2014
Studies of computer science at the TU Dortmund University. Finished with Diploma with honours.
10/2009-06/2013
Studies of statistics at the TU Dortmund University. Finished with Bachelor of Science.

Work History

Since 02/2015
Research Assistant, Information Systems and Statistics Group at the University of Münster, Germany
12/2014-01/2015
Graduate Student Assistant, Information Systems and Statistics Group at the University of Münster, Germany
10/2010-12/2014
Student Assistant, Chair for Computational Statistics, Faculty of Statistics at the TU Dortmund University, Germany.

Publications

2019

  1. 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, East Lansing, Michigan, USA, 2019.
    • BibTex
      @inproceedings{Bossek2019,
        address = {East Lansing, Michigan, USA},
        author = {Bossek, Jakob and Grimme, Christian and Meisel, Stephan and Rudolph, G{\"{u}}nter and Trautmann, Heike},
        booktitle = {Proceedings of the 10th International Conference on Evolutionary Multi-Criterion Optimization},
        series = {EMO},
        title = {{Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm}},
        year = {2019}
      }
      

2018

  1. C. Grimme and J. Bossek, Einführung in die Optimierung: Konzepte, Methoden und Anwendungen. Springer Fachmedien Wiesbaden, 2018 [Online]. Available at: https://books.google.de/books?id=Lhw5ugEACAAJ
    • BibTex
      @book{grimme2018einführung,
        author = {Grimme, C and Bossek, J},
        isbn = {9783658211509},
        publisher = {Springer Fachmedien Wiesbaden},
        title = {{Einf{\"{u}}hrung in die Optimierung: Konzepte, Methoden und Anwendungen}},
        url = {https://books.google.de/books?id=Lhw5ugEACAAJ},
        year = {2018}
      }
      
  2. J. Bossek, “Performance Assessment of Multi-objective Evolutionary Algorithms with the R Package Ecr,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion, Kyoto, Japan, 2018, pp. 1350–1356 [Online]. Available at: http://doi.acm.org/10.1145/3205651.3208312
    • DOI
    • Abstract
    • BibTex
      @inproceedings{Bossek2018PerformanceAssessment,
        address = {Kyoto, Japan},
        author = {Bossek, Jakob},
        booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
        doi = {10.1145/3205651.3208312},
        file = {:Users/jboss/Documents/Mendeley Desktop/Bossek/Proceedings of the 20th Genetic and Evolutionary Computation Conference (GECCO) Companion/Bossek{\_}2018{\_}Performance Assessment of Multi-Objective Evolutionary Algorithms With the R Package ecr{\_}Proceedings of the 20th Genetic and.pdf:pdf},
        isbn = {978-1-4503-5764-7},
        keywords = {evolutionary optimization,performance assessment,software-tools},
        pages = {1350--1356},
        publisher = {ACM},
        series = {GECCO '18},
        title = {{Performance Assessment of Multi-objective Evolutionary Algorithms with the R Package Ecr}},
        url = {http://doi.acm.org/10.1145/3205651.3208312},
        year = {2018}
      }
      
  3. J. Bossek, C. Grimme, S. Meisel, G. Rudolph, and H. Trautmann, “Local Search Effects in Bi-Objective Orienteering,” in Proceedings of the 20th Genetic and Evolutionary Computation Conference (GECCO), Kyoto, Japan, 2018, pp. 585–592 [Online]. Available at: https://doi.org/10.1145/3205455.3205548
    • DOI
    • Abstract
    • BibTex
      @inproceedings{BGMRT2018LocalSearch,
        address = {Kyoto, Japan},
        annote = {Publication status: Accepted},
        author = {Bossek, Jakob and Grimme, Christian and Meisel, Stephan and Rudolph, Guenter and Trautmann, Heike},
        booktitle = {Proceedings of the 20th Genetic and Evolutionary Computation Conference (GECCO)},
        doi = {10.1145/3205455.3205548},
        file = {:Users/jboss/Documents/Mendeley Desktop/Bossek et al/Proceedings of the 20th Genetic and Evolutionary Computation Conference (GECCO)/Bossek et al.{\_}2018{\_}Local Search Effects in Bi-Objective Orienteering{\_}Proceedings of the 20th Genetic and Evolutionary Computation Confer.pdf:pdf},
        mendeley-groups = {Dissertation},
        pages = {585--592},
        publisher = {ACM},
        title = {{Local Search Effects in Bi-Objective Orienteering}},
        url = {https://doi.org/10.1145/3205455.3205548},
        year = {2018}
      }
      
  4. 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 20th Genetic and Evolutionary Computation Conference (GECCO) Companion, Kyoto, Japan, 2018, pp. 1737–1744 [Online]. Available at: http://doi.acm.org/10.1145/3205651.3208233
    • DOI
    • Abstract
    • BibTex
      @inproceedings{KBT2018Parameterization,
        address = {Kyoto, Japan},
        annote = {Publication status: Accepted},
        author = {Kerschke, Pascal and Bossek, Jakob and Trautmann, Heike},
        booktitle = {Proceedings of the 20th Genetic and Evolutionary Computation Conference (GECCO) Companion},
        doi = {10.1145/3205651.3208233},
        file = {:Users/jboss/Documents/Mendeley Desktop/Kerschke, Bossek, Trautmann/Proceedings of the 20th Genetic and Evolutionary Computation Conference (GECCO) Companion/Kerschke, Bossek, Trautmann{\_}2018{\_}Parameterization of State-of-the-Art Performance Indicators A Robustness Study Based on Inexact TSP Sol.pdf:pdf},
        mendeley-groups = {Dissertation},
        pages = {1737--1744},
        publisher = {ACM},
        title = {{Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers}},
        url = {http://doi.acm.org/10.1145/3205651.3208233},
        year = {2018}
      }
      
  5. J. Bossek and H. Trautmann, “Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time,” in Proceedings of the 12th International Conference on Learning and Intelligent Optimization (LION), Kalamata, Greece, 2018.
    • Abstract
    • BibTex
      @inproceedings{BT2018MultiObjectivePerformance,
        address = {Kalamata, Greece},
        author = {Bossek, Jakob and Trautmann, Heike},
        booktitle = {Proceedings of the 12th International Conference on Learning and Intelligent Optimization (LION)},
        file = {:Users/jboss/Documents/Mendeley Desktop/Bossek, Trautmann/Proceedings of the 12th International Conference on Learning and Intelligent Optimization (LION)/Bossek, Trautmann{\_}2018{\_}Multi-Objective Performance Measurement Alternatives to PAR10 and Expected Running Time{\_}Proceedings of the 12th I.pdf:pdf},
        mendeley-groups = {Dissertation},
        publisher = {Springer International Publishing},
        title = {{Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time}},
        year = {2018}
      }
      
  6. J. Bossek and C. Grimme, “Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-Criteria Shortest Path Problems,” in Proceedings of the 12th International Conference on Learning and Intelligent Optimization (LION), Kalamata, Greece, 2018.
    • Abstract
    • BibTex
      @inproceedings{BG2018SolvingScalarized,
        address = {Kalamata, Greece},
        author = {Bossek, Jakob and Grimme, Christian},
        booktitle = {Proceedings of the 12th International Conference on Learning and Intelligent Optimization (LION)},
        file = {:Users/jboss/Documents/Mendeley Desktop/Bossek, Grimme/Proceedings of the 12th International Conference on Learning and Intelligent Optimization (LION)/Bossek, Grimme{\_}2018{\_}Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-Criteria Shortest Path Problems{\_}Proceedings.pdf:pdf},
        mendeley-groups = {Dissertation},
        publisher = {Springer International Publishing},
        title = {{Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-Criteria Shortest Path Problems}},
        year = {2018}
      }
      
  7. J. Bossek, “grapherator: A Modular Multi-Step Graph Generator,” The Journal of Open Source Software, vol. 3, no. 22, p. 528, 2018 [Online]. Available at: http://joss.theoj.org/papers/10.21105/joss.00528
    • Paper
    • DOI
    • BibTex
      @article{B2018grapherator,
        author = {Bossek, Jakob},
        doi = {10.21105/joss.00528},
        file = {:Users/jboss/Documents/Mendeley Desktop/Bossek/The Journal of Open Source Software/Bossek{\_}2018{\_}grapherator A Modular Multi-Step Graph Generator{\_}The Journal of Open Source Software.pdf:pdf},
        issn = {2475-9066},
        journal = {The Journal of Open Source Software},
        mendeley-groups = {LION2018{\_}mcSPP,Dissertation},
        number = {22},
        pages = {528},
        title = {{grapherator: A Modular Multi-Step Graph Generator}},
        url = {http://joss.theoj.org/papers/10.21105/joss.00528},
        volume = {3},
        year = {2018}
      }
      

2017

  1. P. Kerschke, L. Kotthoff, J. Bossek, H. H. Hoos, and H. Trautmann, “Leveraging TSP Solver Complementarity through Machine Learning,” Evolutionary Computation, vol. 0, no. 0, pp. 1–24, 2017.
    • DOI
    • Abstract
    • BibTex
      @article{KKBHTLeveragingTSP,
        annote = {PMID: 28836836},
        author = {Kerschke, Pascal and Kotthoff, Lars and Bossek, Jakob and Hoos, Holger H and Trautmann, Heike},
        doi = {10.1162/evco_a_00215},
        file = {:Users/jboss/Documents/Mendeley Desktop/Kerschke et al/Evolutionary Computation/Kerschke et al.{\_}2017{\_}Leveraging TSP Solver Complementarity through Machine Learning{\_}Evolutionary Computation.pdf:pdf;:Users/jboss/Documents/Mendeley Desktop/Kerschke et al/Evolutionary Computation/Kerschke et al.{\_}2017{\_}Leveraging TSP Solver Complementarity through Machine Learning{\_}Evolutionary Computation(2).pdf:pdf},
        journal = {Evolutionary Computation},
        mendeley-groups = {Dissertation},
        number = {0},
        pages = {1--24},
        title = {{Leveraging TSP Solver Complementarity through Machine Learning}},
        volume = {0},
        year = {2017}
      }
      
  2. 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
    • Paper
    • Abstract
    • BibTex
      @article{B2017Smoof,
        author = {Bossek, Jakob},
        file = {:Users/jboss/Documents/Mendeley Desktop/Bossek/The R Journal/Bossek{\_}2017{\_}smoof Single-and Multi-Objective Optimization Test Functions{\_}The R Journal(2).pdf:pdf},
        journal = {The R Journal},
        number = {1},
        pages = {103--113},
        title = {{smoof: Single-and Multi-Objective Optimization Test Functions}},
        url = {https://journal.r-project.org/archive/2017/RJ-2017-004/RJ-2017-004.pdf},
        volume = {9},
        year = {2017}
      }
      
  3. 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.
    • DOI
    • Abstract
    • BibTex
      @inproceedings{BG2017ExtendedMutation,
        address = {Honolulu, HI, USA},
        author = {Bossek, Jakob and Grimme, Christian},
        booktitle = {2017 IEEE Symposium Series on Computational Intelligence (SSCI)},
        doi = {10.1109/SSCI.2017.8285224},
        file = {:Users/jboss/Documents/Mendeley Desktop/Bossek, Grimme/2017 IEEE Symposium Series on Computational Intelligence (SSCI)/Bossek, Grimme{\_}2017{\_}An Extended Mutation-Based Priority-Rule Integration Concept for Multi-Objective Machine Scheduling{\_}2017 IEEE Sympos.pdf:pdf},
        mendeley-groups = {LION2018{\_}mcSPP,Dissertation},
        pages = {3288--3295},
        publisher = {IEEE},
        title = {{An Extended Mutation-Based Priority-Rule Integration Concept for Multi-Objective Machine Scheduling}},
        year = {2017}
      }
      
  4. J. Bossek, “mcMST: A Toolbox for the Multi-Criteria Minimum Spanning Tree Problem,” The Journal of Open Source Software, vol. 2, no. 17, pp. 1–2, 2017 [Online]. Available at: http://joss.theoj.org/papers/10.21105/joss.00374
    • Paper
    • DOI
    • BibTex
      @article{B2017mcMST,
        author = {Bossek, Jakob},
        doi = {10.21105/joss.00374},
        file = {:Users/jboss/Documents/Mendeley Desktop/Bossek/The Journal of Open Source Software/Bossek{\_}2017{\_}mcMST A Toolbox for the Multi-Criteria Minimum Spanning Tree Problem{\_}The Journal of Open Source Software.pdf:pdf},
        issn = {2475-9066},
        journal = {The Journal of Open Source Software},
        mendeley-groups = {Dissertation},
        number = {17},
        pages = {1--2},
        title = {{mcMST: A Toolbox for the Multi-Criteria Minimum Spanning Tree Problem}},
        url = {http://joss.theoj.org/papers/10.21105/joss.00374},
        volume = {2},
        year = {2017}
      }
      
  5. 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.
    • Paper
    • DOI
    • Abstract
    • BibTex
      @inproceedings{BG2017ParetoBeneficial,
        address = {Honolulu, HI, USA},
        author = {Bossek, Jakob and Grimme, Christian},
        booktitle = {2017 IEEE Symposium Series on Computational Intelligence (SSCI)},
        doi = {10.1109/SSCI.2017.8285183},
        file = {:Users/jboss/Documents/Mendeley Desktop/Bossek, Grimme/2017 IEEE Symposium Series on Computational Intelligence (SSCI)/Bossek, Grimme{\_}2017{\_}A Pareto-Beneficial Sub-Tree Mutation for the Multi-Criteria Minimum Spanning Tree Problem{\_}2017 IEEE Symposium Serie.pdf:pdf},
        mendeley-groups = {LION2018{\_}mcSPP,EvoSoft2018{\_}ecr,Dissertation,STSM},
        pages = {3280--3287},
        publisher = {IEEE},
        title = {{A Pareto-Beneficial Sub-Tree Mutation for the Multi-Criteria Minimum Spanning Tree Problem}},
        year = {2017}
      }
      
  6. 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
    • Paper
    • DOI
    • Abstract
    • BibTex
      @inproceedings{B2017ecr,
        address = {Berlin, Germany},
        author = {Bossek, Jakob},
        booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) Companion},
        doi = {10.1145/3067695.3082470},
        file = {:Users/jboss/Documents/Mendeley Desktop/Bossek/Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) Companion/Bossek{\_}2017{\_}Ecr 2.0 A Modular Framework for Evolutionary Computation in R{\_}Proceedings of the Genetic and Evolutionary Computation Confer.pdf:pdf},
        mendeley-groups = {EvoSoft2018{\_}ecr,Dissertation},
        pages = {1187--1193},
        publisher = {ACM},
        title = {{Ecr 2.0: A Modular Framework for Evolutionary Computation in R}},
        url = {http://doi.acm.org/10.1145/3067695.3082470},
        year = {2017}
      }
      
  7. G. Casalicchio et al., “OpenML: An R Package to Connect to the Networked Machine Learning Platform OpenML,” Computational Statistics, vol. 32, no. 3, pp. 1–15, 2017 [Online]. Available at: https://link.springer.com/article/10.1007/s00180-017-0742-2
    • DOI
    • Abstract
    • BibTex
      @article{CBL2017OpenML,
        author = {Casalicchio, Giuseppe and Bossek, Jakob and Lang, Michel and Kirchhoff, Dominik and Kerschke, Pascal and Hofner, Benjamin and Seibold, Heidi and Vanschoren, Joaquin and Bischl, Bernd},
        doi = {10.1007/s00180-017-0742-2},
        file = {:Users/jboss/Documents/Mendeley Desktop/Casalicchio et al/Computational Statistics/Casalicchio et al.{\_}2017{\_}OpenML An R Package to Connect to the Networked Machine Learning Platform OpenML{\_}Computational Statistics(2).pdf:pdf},
        journal = {Computational Statistics},
        mendeley-groups = {Dissertation},
        number = {3},
        pages = {1--15},
        title = {{OpenML: An R Package to Connect to the Networked Machine Learning Platform OpenML}},
        url = {https://link.springer.com/article/10.1007/s00180-017-0742-2},
        volume = {32},
        year = {2017}
      }
      

2016

  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,” 2016 [Online]. Available at: http://arxiv.org/abs/1703.03373
    • Paper
    • Abstract
    • BibTex
      @article{BRB2017mlrMBO,
        archiveprefix = {arXiv},
        arxivid = {stat/1703.03373},
        author = {Bischl, Bernd and Richter, Jakob and Bossek, Jakob and Horn, Daniel and Thomas, Janek and Lang, Michel},
        eprint = {1703.03373},
        file = {:Users/jboss/Documents/Mendeley Desktop/Bischl et al/Unknown/Bischl et al.{\_}2016{\_}mlrMBO A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions{\_}Unknown.pdf:pdf},
        primaryclass = {stat},
        title = {{mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions}},
        url = {http://arxiv.org/abs/1703.03373},
        year = {2016}
      }
      
  2. 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.
    • DOI
    • Abstract
    • BibTex
      @inproceedings{BT2016UnderstandingCharacteristics,
        address = {Genova, Italy},
        author = {Bossek, Jakob and Trautmann, Heike},
        booktitle = {AI*IA 2016 Advances in Artificial Intelligence},
        doi = {10.1007/978-3-319-49130-1_1},
        editor = {Adorni, G. and Cagnoni, S. and Gori, M. and Maratea, M.},
        file = {:Users/jboss/Documents/Mendeley Desktop/Bossek, Trautmann/AIIA 2016 Advances in Artificial Intelligence/Bossek, Trautmann{\_}2016{\_}Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performa.pdf:pdf},
        isbn = {9783319491295},
        issn = {16113349},
        keywords = {Combinatorial optimization,Instance hardness,Metaheuristics,TSP,Transportation},
        mendeley-groups = {Dissertation},
        pages = {3--12},
        publisher = {Springer International Publishing},
        title = {{Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference}},
        volume = {10037 LNAI},
        year = {2016}
      }
      
  3. J. Bossek and H. Trautmann, “Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers,” in Proceedings of the 10th International Conference on Learning and Intelligent Optimization (LION 2016), Ischia, Italy, 2016, vol. 10079 LNCS, pp. 48–59.
    • DOI
    • Abstract
    • BibTex
      @inproceedings{BT2016EvolvingInstances,
        address = {Ischia, Italy},
        author = {Bossek, Jakob and Trautmann, Heike},
        booktitle = {Proceedings of the 10th International Conference on Learning and Intelligent Optimization (LION 2016)},
        doi = {10.1007/978-3-319-50349-3_4},
        editor = {Festa, Paola and Sellmann, Meinolf and Vanschoren, Joaquin},
        file = {:Users/jboss/Documents/Mendeley Desktop/Bossek, Trautmann/Proceedings of the 10th International Conference on Learning and Intelligent Optimization (LION 2016)/Bossek, Trautmann{\_}2016{\_}Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers{\_}Proceedings of.pdf:pdf},
        isbn = {9783319503486},
        issn = {16113349},
        keywords = {Algorithm selection,Feature selection,Instance hardness,TSP},
        mendeley-groups = {Dissertation},
        pages = {48--59},
        publisher = {Springer International Publishing},
        title = {{Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers}},
        volume = {10079 LNCS},
        year = {2016}
      }
      

2015

  1. 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.
    • Paper
    • DOI
    • Abstract
    • BibTex
      @inproceedings{MGBTR2015EvaluationOrienteering,
        address = {New York, NY, USA},
        author = {Meisel, Stephan and Grimme, Christian and Bossek, Jakob and W{\"{o}}lck, Martin and Rudolph, G{\"{u}}nter and Trautmann, Heike},
        booktitle = {Proceedings of the 17th Genetic and Evolutionary Computation Conference (GECCO)},
        doi = {10.1145/2739480.2754705},
        file = {:Users/jboss/Documents/Mendeley Desktop/Meisel et al/Proceedings of the 17th Genetic and Evolutionary Computation Conference (GECCO)/Meisel et al.{\_}2015{\_}Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle{\_}Proceedings of the 17th Ge.pdf:pdf},
        isbn = {9781450334723},
        keywords = {Combinatorial optimization,Metaheuristics},
        mendeley-groups = {Dissertation},
        pages = {425--432},
        publisher = {ACM},
        title = {{Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle}},
        year = {2015}
      }
      
  2. 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 ’15), Madrid, Spain, 2015, pp. 1319–1326 [Online]. Available at: http://dl.acm.org/citation.cfm?doid=2739480.2754673
    • DOI
    • Abstract
    • BibTex
      @inproceedings{BBWR2015LearningFeature,
        address = {Madrid, Spain},
        author = {Bossek, Jakob and Bischl, Bernd and Wagner, Tobias and Rudolph, G{\"{u}}nter},
        booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '15)},
        doi = {10.1145/2739480.2754673},
        file = {:Users/jboss/Documents/Mendeley Desktop/Bossek et al/Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '15)/Bossek et al.{\_}2015{\_}Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement{\_}Proceedings of the Gene.pdf:pdf},
        isbn = {9781450334723},
        keywords = {evolutionary algorithm,model-based optimization,parameter tuning},
        pages = {1319--1326},
        title = {{Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement}},
        url = {http://dl.acm.org/citation.cfm?doid=2739480.2754673},
        year = {2015}
      }
      

2013

  1. 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.
    • Paper
    • DOI
    • Abstract
    • BibTex
      @article{MBT2013NovelApproach,
        archiveprefix = {arXiv},
        arxivid = {1208.2318},
        author = {Mersmann, Olaf and Bischl, Bernd and Trautmann, Heike and Wagner, Markus and Bossek, Jakob and Neumann, Frank},
        doi = {10.1007/s10472-013-9341-2},
        eprint = {1208.2318},
        file = {:Users/jboss/Documents/Mendeley Desktop/Mersmann et al/Annals of Mathematics and Artificial Intelligence/Mersmann et al.{\_}2013{\_}A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem{\_}Annals o.pdf:pdf},
        issn = {10122443},
        journal = {Annals of Mathematics and Artificial Intelligence},
        keywords = {2-opt,Classification,Feature selection,MARS,TSP},
        number = {2},
        pages = {151--182},
        title = {{A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem}},
        volume = {69},
        year = {2013}
      }
      
  2. 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.
    • DOI
    • Abstract
    • BibTex
      @article{Mersmann2013,
        archiveprefix = {arXiv},
        arxivid = {1208.2318},
        author = {Mersmann, Olaf and Bischl, Bernd and Trautmann, Heike and Wagner, Markus and Bossek, Jakob and Neumann, Frank},
        doi = {10.1007/s10472-013-9341-2},
        eprint = {1208.2318},
        file = {:Users/jboss/Documents/Mendeley Desktop/Mersmann et al/Annals of Mathematics and Artificial Intelligence/Mersmann et al.{\_}2013{\_}A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem{\_}Annals o.pdf:pdf},
        issn = {10122443},
        journal = {Annals of Mathematics and Artificial Intelligence},
        keywords = {2-opt,Classification,Feature selection,MARS,TSP},
        number = {2},
        pages = {151--182},
        title = {{A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem}},
        volume = {69},
        year = {2013}
      }
      

2012

  1. 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,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7219 LNCS, pp. 115–129, 2012.
    • Paper
    • DOI
    • Abstract
    • BibTex
      @article{MBB2012LocalSearchTSP,
        author = {Mersmann, Olaf and Bischl, Bernd and Bossek, Jakob and Trautmann, Heike and Wagner, Markus and Neumann, Frank},
        doi = {10.1007/978-3-642-34413-8_9},
        file = {:Users/jboss/Documents/Mendeley Desktop/Mersmann et al/Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)/Mersmann et al.{\_}2012{\_}Local search and the traveling salesman problem A feature-based characterization of problem hardness{\_}Lecture Notes.pdf:pdf},
        isbn = {9783642344121},
        issn = {03029743},
        journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
        keywords = {2-opt,Classification,Feature Selection,MARS,TSP},
        pages = {115--129},
        title = {{Local search and the traveling salesman problem: A feature-based characterization of problem hardness}},
        volume = {7219 LNCS},
        year = {2012}
      }