Staff directory

VERMETTEN Diederick

Postdoc
Team : RO

Sorbonne Université - LIP6

+33 1 44 27 87 40
Diederick.Vermetten (at) nulllip6.fr
https://lip6.fr/Diederick.Vermetten

2019-2024 Publications

2024
- A. Kostovska, D. Vermetten, P. Korosec, S. Dzeroski, C. Doerr, T. Eftimov : “Using Machine Learning Methods to Assess Module Performance Contribution in Modular Optimization Frameworks”, Evolutionary Computation, pp. 1-28, (Massachusetts Institute of Technology Press (MIT Press)) (2024)
- 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) (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 (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) (2024)
- J. De Nobel, F. Ye, D. Vermetten, H. Wang, C. Doerr, Th. Bäck : “IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics”, Evolutionary Computation, pp. 1-6, (Massachusetts Institute of Technology Press (MIT Press)) (2024)
- M. López‑Ibañez, D. Vermetten, J. Dréo, C. Doerr : “Using the Empirical Attainment Function for Analyzing Single-objective Black-box Optimization Algorithms”, IEEE Transactions on Evolutionary Computation, (Institute of Electrical and Electronics Engineers) (2024)
- 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 (2024)
- D. Vermetten, F. Ye, Th. Bäck, C. Doerr : “MA-BBOB: A Problem Generator for Black-Box Optimization Using Affine Combinations and Shifts”, ACM Transactions on Evolutionary Learning and Optimization, (ACM) (2024)
2023
- A. Kostovska, D. Vermetten, C. Doerr, S. Dzeroski, T. Eftimov : “OPTION: OPTImization Algorithm Benchmarking ONtology”, IEEE Transactions on Evolutionary Computation, vol. 27 (6), pp. 1618-1632, (Institute of Electrical and Electronics Engineers) (2023)
- A. Nikolikj, G. Cenikj, G. Ispirova, D. Vermetten, R. Lang, A. Engelbrecht, C. Doerr, P. Korosec, T. Eftimov : “Assessing the Generalizability of a Performance Predictive Model”, GECCO '23 Companion: Companion Conference on Genetic and Evolutionary Computation, Lisbon, Portugal, pp. 311-314, (ACM) (2023)
- A. Kostovska, A. Janković, D. Vermetten, S. Dzeroski, T. Eftimov, C. Doerr : “Comparing Algorithm Selection Approaches on Black-Box Optimization Problems”, GECCO '23 Companion: Companion Conference on Genetic and Evolutionary Computation, Lisbon, Portugal, pp. 495-498, (ACM) (2023)
- C. Doerr, H. Wang, D. Vermetten, Th. Bäck, J. De Nobel, F. Ye : “Benchmarking and analyzing iterative optimization heuristics with IOHprofiler”, GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation, Lisbon, Portugal, pp. 938-945, (ACM) (2023)
- D. Vermetten, F. Ye, C. Doerr : “Using Affine Combinations of BBOB Problems for Performance Assessment”, Proceedings of the Genetic and Evolutionary Computation Conference, Lisbon, Portugal, pp. 873-881, (ACM), (ISBN: 9798400701191) (2023)
- A. Kostovska, D. Vermetten, S. Dzeroski, P. Panov, T. Eftimov, C. Doerr : “Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithms”, Applications of Evolutionary Computation (EvoApplications 2023), vol. 13989, Lecture Notes in Computer Science, Brno, Czechia, pp. 253-268, (Springer), (ISBN: 978-3-031-30229-9) (2023)
- D. Vermetten, F. Ye, Th. Bäck, C. Doerr : “MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization Contexts”, Proceedings of Machine Learning Research (PMLR), Potsdam, Germany (2023)
- A. Kostovska, G. Cenikj, D. Vermetten, A. Janković, A. Nikolikj, U. Skvorc, P. Korosec, C. Doerr, T. Eftimov : “PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization”, Proceedings of Machine Learning Research (PMLR), Potsdam, Germany (2023)
- F. Clément, D. Vermetten, J. De Nobel, A. Jesus, L. Paquete, C. Doerr : “Computing Star Discrepancies with Numerical Black-Box Optimization Algorithms”, GECCO '23: Genetic and Evolutionary Computation Conference, Lisbon, Portugal, pp. 1330-1338, (ACM) (2023)
2022
- H. Wang, D. Vermetten, F. Ye, C. Doerr, Th. Bäck : “IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics”, ACM Transactions on Evolutionary Learning and Optimization, vol. 2 (1), pp. 3:1-3:29, (ACM) (2022)
- F. Ye, D. Vermetten, C. Doerr, Th. Bäck : “Non-Elitist Selection Can Improve the Performance of Irace”, 17^th Proceedings of Parallel Problem Solving from Nature - (PPSN) 2022, Dortmund, Germany, pp. 32-45 (2022)
- A. Janković, D. Vermetten, A. Kostovska, J. De Nobel, T. Eftimov, C. Doerr : “Trajectory-based Algorithm Selection with Warm-starting”, 2022 IEEE Congress on Evolutionary Computation (CEC), 2022 IEEE Congress on Evolutionary Computation (CEC), Padua, Italy, pp. 1-8, (IEEE) (2022)
- D. Vermetten, H. Wang, M. López‑Ibañez, C. Doerr, Th. Bäck : “Analyzing the Impact of Undersampling on the Benchmarking and Configuration of Evolutionary Algorithms”, GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference, Boston, United States (2022)
- A. Kostovska, D. Vermetten, S. Dzeroski, C. Doerr, P. Korosec, T. Eftimov : “The Importance of Landscape Features for Performance Prediction of Modular CMA-ES Variants”, GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference, Boston, United States (2022)
- A. Kostovska, A. Janković, D. Vermetten, J. De Nobel, H. Wang, T. Eftimov, C. Doerr : “Per-run Algorithm Selection with Warm-starting using Trajectory-based Features”, 17^th Proceedings of Parallel Problem Solving from Nature - (PPSN) 2022, Dortmund, Germany, pp. 46-60 (2022)
- C. Doerr, H. Wang, D. Vermetten, Th. Bäck, J. De Nobel, F. Ye : “Benchmarking and analyzing iterative optimization heuristics with IOHprofiler (GECCO’22 tutorial slides)”, (2022)
2021
- A. Kostovska, D. Vermetten, C. Doerr, S. Dzeroski, P. Panov, T. Eftimov : “OPTION: OPTImization Algorithm Benchmarking ONtology”, Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2021, Companion Material), Lille (on line), France (2021)
- J. De Nobel, D. Vermetten, H. Wang, C. Doerr, Th. Bäck : “Tuning as a Means of Assessing the Benefits of New Ideas in Interplay with Existing Algorithmic Modules”, Genetic and Evolutionary Computation Conference (GECCO 2021, Companion Material, Workshop), Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2021, Companion Material, Workshop Paper), Lille (en ligne), France (2021)
2020
- D. Vermetten, H. Wang, Th. Bäck, C. Doerr : “Towards Dynamic Algorithm Selection for Numerical Black-Box Optimization: Investigating BBOB as a Use Case”, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'20), Cancun, Mexico (2020)
- D. Vermetten, H. Wang, C. Doerr, Th. Bäck : “Integrated vs. Sequential Approaches for Selecting and Tuning CMA-ES Variants”, ACM Genetic and Evolutionary Computation Conference (GECCO'20), Proc. of ACM Genetic and Evolutionary Computation Conference (GECCO'20), Cancun, Mexico (2020)
2019
- D. Vermetten, S. Van Rijn, Th. Bäck, C. Doerr : “Online selection of CMA-ES variants”, GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference, Prague, Czechia, pp. 951-959, (ACM Press) (2019)