Please note that LibreCat no longer supports Internet Explorer versions 8 or 9 (or earlier).
We recommend upgrading to the latest Internet Explorer, Google Chrome, or Firefox.
197 Publications
2020 | Conference Paper | LibreCat-ID: 46330
Seiler, Moritz, et al. “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem.” Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI), edited by Thomas Bäck et al., 2020, pp. 48–64, doi:10.1007/978-3-030-58112-1_4.
LibreCat
| DOI
2020 | Journal Article | LibreCat-ID: 46334
Bossek, Jakob, et al. “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms.” Applied Soft Computing, vol. 88, 2020, p. 105901, doi:https://doi.org/10.1016/j.asoc.2019.105901.
LibreCat
| DOI
2020 | Conference Paper | LibreCat-ID: 46322
Bossek, Jakob, et al. “Towards Decision Support in Dynamic Bi-Objective Vehicle Routing.” Proceedings of the IEEE Congress on Evolutionary Computation (CEC), 2020, pp. 1–8, doi:10.1109/CEC48606.2020.9185778.
LibreCat
| DOI
2020 | Conference Paper | LibreCat-ID: 46324
Bossek, Jakob, et al. “Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection.” Proceedings of the IEEE Congress on Evolutionary Computation (CEC), IEEE, 2020, pp. 1–8.
LibreCat
2020 | Conference Paper | LibreCat-ID: 46323
Bossek, Jakob, et al. “Dynamic Bi-Objective Routing of Multiple Vehicles.” Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20), ACM, 2020, pp. 166–174.
LibreCat
2019 | Conference Paper | LibreCat-ID: 46343
Grimme, Christian, et al. “Multimodality in Multi-Objective Optimization — More Boon than Bane?” Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO), edited by Kalyanmoy Deb et al., vol. 11411, Springer, 2019, pp. 126–138, doi:10.1007/978-3-030-12598-1_11.
LibreCat
| DOI
2019 | Journal Article | LibreCat-ID: 46345
Kerschke, Pascal, et al. “Automated Algorithm Selection: Survey and Perspectives.” Evolutionary Computation (ECJ), vol. 27, no. 1, 2019, pp. 3–45, doi:10.1162/evco_a_00242.
LibreCat
| DOI
2019 | Journal Article | LibreCat-ID: 46344
Carnein, Matthias, and Heike Trautmann. “Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms.” Business and Information Systems Engineering (BISE), vol. 61, no. 3, 2019, pp. 277–297.
LibreCat
2019 | Conference Paper | LibreCat-ID: 46340
Carnein, Matthias, et al. “A Recommender System Based on Omni-Channel Customer Data.” Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19), 2019, pp. 65–74.
LibreCat
2019 | Conference Paper | LibreCat-ID: 46341
Carnein, Matthias, and Heike Trautmann. “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), 2019, pp. 280–292.
LibreCat
2019 | Conference Paper | LibreCat-ID: 46342
Grimme, Christian, et al. “Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization.” AIP Conference Proceedings, AIP Publishing, 2019, pp. 020052-1-020052–54, doi:10.1063/1.5090019.
LibreCat
| DOI
2019 | Book Chapter | LibreCat-ID: 46336
Kerschke, Pascal, and Heike Trautmann. “Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package Flacco.” Applications in Statistical Computing, edited by Nadja Bauer et al., Springer, 2019, pp. 93–123, doi:10.1007/978-3-030-25147-5_7.
LibreCat
| DOI
2019 | Book | LibreCat-ID: 46335
Trautmann, Heike. Applications in Statistical Computing — From Music Data Analysis to Industrial Quality Improvement. Springer International Publishing, 2019.
LibreCat
2019 | Journal Article | LibreCat-ID: 46346
Kerschke, Pascal, and Heike Trautmann. “Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning.” Evolutionary Computation (ECJ), vol. 27, no. 1, 2019, pp. 99–127, doi:10.1162/evco_a_00236.
LibreCat
| DOI
2019 | Journal Article | LibreCat-ID: 46347
Kerschke, Pascal, et al. “Search Dynamics on Multimodal Multi-Objective Problems.” Evolutionary Computation (ECJ), vol. 27, no. 4, 2019, pp. 577–609, doi:10.1162/evco_a_00234.
LibreCat
| DOI
2019 | Conference Paper | LibreCat-ID: 48841
Bossek, Jakob, et al. “Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm.” Evolutionary Multi-Criterion Optimization (EMO), edited by Kalyanmoy Deb et al., Springer International Publishing, 2019, pp. 516–528, doi:10.1007/978-3-030-12598-1_41.
LibreCat
| DOI
2019 | Conference Paper | LibreCat-ID: 48842
Bossek, Jakob, et al. “Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators.” Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, Association for Computing Machinery, 2019, pp. 58–71, doi:10.1145/3299904.3340307.
LibreCat
| DOI
2019 | Conference Paper | LibreCat-ID: 48843
Bossek, Jakob, et al. “Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring.” Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, 2019, pp. 1443–1451, doi:10.1145/3321707.3321792.
LibreCat
| DOI
2019 | Conference Paper | LibreCat-ID: 48840
Bossek, Jakob, et al. “On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem.” Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, 2019, pp. 516–523, doi:10.1145/3321707.3321818.
LibreCat
| DOI
2019 | Conference Paper | LibreCat-ID: 48858
Bossek, Jakob, and Christian Grimme. “Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-Criteria Shortest Path Problems.” Learning and Intelligent Optimization, edited by Roberto Battiti et al., Springer International Publishing, 2019, pp. 184–198, doi:10.1007/978-3-030-05348-2_17.
LibreCat
| DOI