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204 Publications
2020 | Conference Paper | LibreCat-ID: 48895
Roostapour, V., Bossek, J., & Neumann, F. (2020). Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem. Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 551–559. https://doi.org/10.1145/3377930.3390168
LibreCat
| DOI
2020 | Conference Paper | LibreCat-ID: 48897
Seiler, M., 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. Parallel Problem Solving from {Nature} (PPSN XVI), 48–64. https://doi.org/10.1007/978-3-030-58112-1_4
LibreCat
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2020 | Journal Article | LibreCat-ID: 48848
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, 88(C). https://doi.org/10.1016/j.asoc.2019.105901
LibreCat
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2020 | Journal Article | LibreCat-ID: 48836
Bartz-Beielstein, T., Doerr, C., van den Berg, D., Bossek, J., Chandrasekaran, S., Eftimov, T., Fischbach, A., Kerschke, P., Cava, W. L., Lopez-Ibanez, 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. Corr.
LibreCat
2020 | Conference Paper | LibreCat-ID: 46331
Seiler, M., Trautmann, H., & Kerschke, P. (2020). Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries. Proceedings of the International Joint Conference on Neural Networks (IJCNN), 1–8. https://doi.org/10.1109/IJCNN48605.2020.9207338
LibreCat
| DOI
2020 | Conference Paper | LibreCat-ID: 46330
Seiler, M., 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 T. Bäck, M. Preuss, A. Deutz, H. Wang, C. Doerr, M. Emmerich, & H. Trautmann (Eds.), Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI) (pp. 48–64). https://doi.org/10.1007/978-3-030-58112-1_4
LibreCat
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2020 | Journal Article | LibreCat-ID: 46334
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, 88, 105901. https://doi.org/10.1016/j.asoc.2019.105901
LibreCat
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2020 | Conference Paper | LibreCat-ID: 46322
Bossek, J., Grimme, C., Rudolph, G., & Trautmann, H. (2020). Towards Decision Support in Dynamic Bi-Objective Vehicle Routing. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), 1–8. https://doi.org/10.1109/CEC48606.2020.9185778
LibreCat
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2020 | Conference Paper | LibreCat-ID: 46324
Bossek, J., Kerschke, P., & Trautmann, H. (2020). Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), 1–8.
LibreCat
2020 | Conference Paper | LibreCat-ID: 46323
Bossek, J., Grimme, C., & Trautmann, H. (2020). Dynamic Bi-Objective Routing of Multiple Vehicles. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20), 166–174.
LibreCat
2019 | Conference Paper | LibreCat-ID: 46343
Grimme, C., Kerschke, P., & Trautmann, H. (2019). Multimodality in Multi-Objective Optimization — More Boon than Bane? In K. Deb, E. Goodman, C. C. A. Coello, K. Klamroth, K. Miettinen, S. Mostaghim, & P. Reed (Eds.), Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO) (Vol. 11411, pp. 126–138). Springer. https://doi.org/10.1007/978-3-030-12598-1_11
LibreCat
| DOI
2019 | Journal Article | LibreCat-ID: 46345
Kerschke, P., Hoos, H. H., Neumann, F., & Trautmann, H. (2019). Automated Algorithm Selection: Survey and Perspectives. Evolutionary Computation (ECJ), 27(1), 3–45. https://doi.org/10.1162/evco_a_00242
LibreCat
| DOI
2019 | Journal Article | LibreCat-ID: 46344
Carnein, M., & Trautmann, H. (2019). Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms. Business and Information Systems Engineering (BISE), 61(3), 277–297.
LibreCat
2019 | Conference Paper | LibreCat-ID: 46340
Carnein, M., Homann, L., Trautmann, H., & Vossen, G. (2019). A Recommender System Based on Omni-Channel Customer Data. Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19), 65–74.
LibreCat
2019 | Conference Paper | LibreCat-ID: 46341
Carnein, M., & Trautmann, H. (2019). Customer Segmentation Based on Transactional Data Using Stream Clustering. Proceedings of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’19), 280–292.
LibreCat
2019 | Conference Paper | LibreCat-ID: 46342
Grimme, C., Kerschke, P., Emmerich, M. T. M., Preuss, M., Deutz, A. H., & Trautmann, H. (2019). Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization. AIP Conference Proceedings, 020052-1-020052–020054. https://doi.org/10.1063/1.5090019
LibreCat
| DOI
2019 | Book Chapter | LibreCat-ID: 46336
Kerschke, P., & Trautmann, H. (2019). Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-package flacco. In N. Bauer, K. Ickstadt, K. Lübke, G. Szepannek, H. Trautmann, & M. Vichi (Eds.), Applications in Statistical Computing (pp. 93–123). Springer. https://doi.org/10.1007/978-3-030-25147-5_7
LibreCat
| DOI
2019 | Book | LibreCat-ID: 46335
Trautmann, H. (2019). Applications in Statistical Computing — From Music Data Analysis to Industrial Quality Improvement. Springer International Publishing.
LibreCat
2019 | Journal Article | LibreCat-ID: 46346
Kerschke, P., & Trautmann, H. (2019). Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning. Evolutionary Computation (ECJ), 27(1), 99–127. https://doi.org/10.1162/evco_a_00236
LibreCat
| DOI
2019 | Journal Article | LibreCat-ID: 46347
Kerschke, P., Wang, H., Preuss, M., Grimme, C., Deutz, A., Trautmann, H., & Emmerich, M. (2019). Search Dynamics on Multimodal Multi-Objective Problems. Evolutionary Computation (ECJ), 27(4), 577–609. https://doi.org/10.1162/evco_a_00234
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