Preuß OL, Rook J, Trautmann H. On the Potential of Multi-objective Automated Algorithm Configuration on Multi-modal Multi-objective Optimisation Problems. In: Applications of Evolutionary Computation. Springer Nature Switzerland; 2024. doi:10.1007/978-3-031-56852-7_20
LibreCat: /record/52759
DOI: 10.1007/978-3-031-56852-7_20

Vinzent Seiler M, Rook J, Heins J, Preuß OL, Bossek J, Trautmann H. Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP. In: 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE; 2024. doi:10.1109/ssci52147.2023.10372008
LibreCat: /record/52749
DOI: 10.1109/ssci52147.2023.10372008

Prager RP, Dietrich K, Schneider L, et al. Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features. In: Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms. FOGA ’23. Association for Computing Machinery; 2023:129–139. doi:10.1145/3594805.3607136
LibreCat: /record/47522
DOI: 10.1145/3594805.3607136

Prager RP, Trautmann H. Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis Features. In: Correia J, Smith S, Qaddoura R, eds. Applications of Evolutionary Computation. Springer Nature Switzerland; 2023:411–425.
LibreCat: /record/46297

Schäpermeier L, Kerschke P, Grimme C, Trautmann H. Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets. In: Emmerich M, Deutz A, Wang H, et al., eds. Evolutionary Multi-Criterion Optimization. Springer Nature Switzerland; 2023:291–304.
LibreCat: /record/46298

Prager RP, Trautmann H. Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python. Evolutionary Computation. Published online 2023:1–25. doi:10.1162/evco_a_00341
LibreCat: /record/46299
DOI: 10.1162/evco_a_00341

Bossek J, Neumann A, Neumann F. On the Impact of Basic Mutation Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO’23. Association for Computing Machinery; 2023:248–256. doi:10.1145/3583131.3590384
LibreCat: /record/48869
DOI: 10.1145/3583131.3590384

Bossek J, Sudholt D. Runtime Analysis of Quality Diversity Algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO’23. Association for Computing Machinery; 2023:1546–1554. doi:10.1145/3583131.3590383
LibreCat: /record/48872
DOI: 10.1145/3583131.3590383

Marrero A, Segredo E, Hart E, Bossek J, Neumann A. Generating Diverse and Discriminatory Knapsack Instances by Searching for Novelty in Variable Dimensions of Feature-Space. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO’23. Association for Computing Machinery; 2023:312–320. doi:10.1145/3583131.3590504
LibreCat: /record/48886
DOI: 10.1145/3583131.3590504

Bossek J, Sudholt D. Do Additional Target Points Speed Up Evolutionary Algorithms? Theoretical Computer Science. Published online 2023:113757. doi:10.1016/j.tcs.2023.113757
LibreCat: /record/48871
DOI: 10.1016/j.tcs.2023.113757

Bossek J, Grimme C. On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem. Evolutionary Computation. Published online 2023:1–35. doi:10.1162/evco_a_00335
LibreCat: /record/48859
DOI: 10.1162/evco_a_00335

Heins J, Bossek J, Pohl J, Seiler M, Trautmann H, Kerschke P. A study on the effects of normalized TSP features for automated algorithm selection. Theoretical Computer Science. 2023;940:123-145. doi:https://doi.org/10.1016/j.tcs.2022.10.019
LibreCat: /record/46310
DOI: https://doi.org/10.1016/j.tcs.2022.10.019

Prager RP, Trautmann H. Investigating the Viability of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems. In: Silva S, Paquete L, eds. Companion Proceedings of the Conference on Genetic and Evolutionary Computation, GECCO 2023, Companion Volume, Lisbon, Portugal, July 15-19, 2023. ACM; 2023:451–454. doi:10.1145/3583133.3590757
LibreCat: /record/52530
DOI: 10.1145/3583133.3590757

Seiler MV, Rook J, Heins J, Preuß OL, Bossek J, Trautmann H. Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP. In: 2023 IEEE Symposium Series on Computational Intelligence (SSCI). ; :361-368. doi:10.1109/SSCI52147.2023.10372008
LibreCat: /record/48898
DOI: 10.1109/SSCI52147.2023.10372008

ckvorc U, Eftimov T, Koro]ec P. Analyzing the Generalizability of Automated Algorithm Selection: A Case Study for Numerical Optimization. In: 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE; 2023. doi:10.1109/ssci52147.2023.10371868
LibreCat: /record/52863
DOI: 10.1109/ssci52147.2023.10371868

Niemann M, Assenmacher D, Brunk J, Riehle DM, Becker J, Trautmann H. (Semi-)Automatische Kommentarmoderation zur Erhaltung Konstruktiver Diskurse. In: Weitzel G, Mündges S, eds. Hate Speech — Definitionen, Ausprägungen, Lösungen. VS Verlag für Sozialwissenschaften; 2022:249–274. doi:10.1007/978-3-658-35658-3_13
LibreCat: /record/46300
DOI: 10.1007/978-3-658-35658-3_13

Assenmacher D, Trautmann H. Textual One-Pass Stream Clustering with Automated Distance Threshold Adaption. In: et al. Tran T, ed. Intelligent Information and Database Systems. Springer International Publishing; 2022:3–16. doi:10.1007/978-3-031-21743-2_1
LibreCat: /record/46301
DOI: 10.1007/978-3-031-21743-2_1

Assenmacher D, Weber D, Preuss M, et al. Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem. Social Science Computer Review. 2022;40(6):1496-1522. doi:10.1177/08944393211012268
LibreCat: /record/46316
DOI: 10.1177/08944393211012268

Schneider L, Schäpermeier L, Prager RP, Bischl B, Trautmann H, Kerschke P. HPO x ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis. In: Rudolph G, Kononova AV, Aguirre H, Kerschke P, Ochoa G, Tusar T, eds. Parallel Problem Solving from Nature — PPSN XVII. Springer International Publishing; 2022:575–589. doi:10.1007/978-3-031-14714-2_40
LibreCat: /record/46306
DOI: 10.1007/978-3-031-14714-2_40

Seiler MV, Prager RP, Kerschke P, Trautmann H. A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes. In: Proceedings of the Genetic and Evolutionary Computation Conference. Association for Computing Machinery; 2022:657–665. doi:10.1145/3512290.3528834
LibreCat: /record/46307
DOI: 10.1145/3512290.3528834