| Current teams : | ALMASTY ALSOC APR BD CIAN ComplexNetworks DECISION DELYS LFI MOCAH MoVe NPA PEQUAN PolSys QI RO SMA SYEL |
| Former team : | ACASA |
| Publications RO | 2023 | 2024 | Total |
|---|---|---|---|
| Books | 0 | 0 | 0 |
| Edited books | 0 | 0 | 0 |
| Journal articles | 15 | 15 | 30 |
| Book chapters | 0 | 0 | 0 |
| Conference papers | 34 | 14 | 48 |
| Habilitations | 0 | 0 | 0 |
| Thesis | 2 | 2 | 4 |
- S. Angelopoulos, Ch. Dürr, A. Elenter, G. Melidi : “Scenario-Based Robust Optimization of Tree Structures”, 39th Annual AAAI Conference on Artificial Intelligence (AAAI-25), Philadelphia (PA), United States [Angelopoulos 2024a]
- S. Angelopoulos, Ch. Dürr, A. Elenter, Y. Lefki : “Overcoming Brittleness in Pareto-Optimal Learning-Augmented Algorithms”, Proceedings of The Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024), Vancouver, BC, Canada [Angelopoulos 2024b]
- S. Angelopoulos, M. Bienkowski, Ch. Dürr, B. Simon : “Contract Scheduling with Distributional and Multiple Advice”, Proceedings of the 33rd International Joint Conference in Artificial Intelligence (IJCAI-24), Jeju Island, Korea, Republic of [Angelopoulos 2024e]
- K. Dietrich, D. Vermetten, C. Doerr, P. Kerschke : “Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization”, GECCO '24: Proceedings of the Genetic and Evolutionary Computation Conference, Melbourne, Australia [Dietrich 2024a]
- K. Dietrich, R. Prager, C. Doerr, H. Trautmann : “Hybridizing Target-and SHAP-encoded Features for Algorithm Selection in Mixed-variable Black-box Optimization”, Parallel Problem Solving from Nature – PPSN XVIII (PPSN 2024), vol. 15149, Lecture Notes in Computer Science, Hagenberg, Austria, pp. 154-169, (Springer Nature Switzerland) [Dietrich 2024b]
- J. Grus, C. Hanen, Z. Hanzalek : “Packing-Inspired Algorithms for Periodic Scheduling Problems with Harmonic Periods”, Proceedings of the 13th International Conference on Operations Research and Enterprise Systems - ICORES, vol. 1, Rome, Italy, pp. 101-112, (SciTePress - Science and Technology Publications), (ISBN: 978-989-758-681-1) [Grus 2024]
- M. Mallem, C. Hanen, A. Munier‑Kordon : “A New Structural Parameter on Single Machine Scheduling with Release Dates and Deadlines”, International Symposium on combinatorial optimization 2024, vol. 14594, Lecture Notes in Computer Science, Tenerife (Canaries), Spain, pp. 205-219, (Springer Nature Switzerland) [Mallem 2024]
- A. Nikolikj, A. Kostovska, D. Vermetten, C. Doerr, T. Eftimov : “Quantifying Individual and Joint Module Impact in Modular Optimization Frameworks”, 2024 IEEE Congress on Evolutionary Computation (CEC), Yokohama, Japan, (IEEE) [Nikolikj 2024a]
- A. Nikolikj, A. Kostovska, G. Cenikj, C. Doerr, T. Eftimov : “Generalization Ability of Feature-based Performance Prediction Models: A Statistical Analysis across Benchmarks”, 2024 IEEE Congress on Evolutionary Computation (CEC), Yokohama, Japan, (IEEE) [Nikolikj 2024b]
- M. Seiler, U. Skvorc, C. Doerr, H. Trautmann : “Synergies of Deep and Classical Exploratory Landscape Features for Automated Algorithm Selection”, The 18th Learning and Intelligent OptimizatioN Conference (LION 2024), Lecture Notes in Computer Science, Ischia, Italy, (Springer) [Seiler 2024a]
- M. Seiler, U. Skvorc, G. Cenikj, C. Doerr, H. Trautmann : “Learned Features vs. Classical ELA on Affine BBOB Functions”, Parallel Problem Solving from Nature – PPSN XVIII (PPSN 2024), vol. 15149, Lecture Notes in Computer Science, Hagenberg, Austria, pp. 137-153, (Springer Nature Switzerland) [Seiler 2024b]
- M. Van den Nieuwenhuijzen, C. Doerr, J. Van Rijn, H. Gouk : “Selecting Pre-trained Models for Transfer Learning with Data-centric Meta-features”, AutoML Conference 2024 (Workshop Track), Paris, France [van den Nieuwenhuijzen 2024]
- D. Vermetten, C. Doerr, H. Wang, A. Kononova, Th. Bäck : “Large-scale Benchmarking of Metaphor-based Optimization Heuristics”, GECCO '24: Proceedings of the Genetic and Evolutionary Computation Conference, Melbourne, Australia [Vermetten 2024a]
- D. Vermetten, J. Lengler, D. Rusin, Th. Bäck, C. Doerr : “Empirical Analysis of the Dynamic Binary Value Problem with IOHprofiler”, Parallel Problem Solving from Nature – PPSN XVIII, vol. 15149, Lecture Notes in Computer Science, Hagenberg, Austria, pp. 20-35, (Springer Nature Switzerland), (ISBN: 978-3-031-70068-2) [Vermetten 2024c]