Online selection of CMA-ES variants. - Diederick Vermetten; Sander van Rijn; Thomas Bäck; Carola Doerr (GECCO, 2019)

Sequential vs. Integrated Algorithm Selection and Configuration: A Case Study for the Modular CMA-ES. - Diederick Vermetten; Hao Wang 025; Carola Doerr; Thomas Bäck (CoRR, 2019)

Towards dynamic algorithm selection for numerical black-box optimization: investigating BBOB as a use case. - Diederick Vermetten; Hao Wang 025; Thomas Bäck; Carola Doerr (GECCO, 2020)

Integrated vs. sequential approaches for selecting and tuning CMA-ES variants. - Diederick Vermetten; Hao Wang 025; Carola Doerr; Thomas Bäck (GECCO, 2020)

IOHanalyzer: Performance Analysis for Iterative Optimization Heuristic. - Hao Wang 025; Diederick Vermetten; Furong Ye; Carola Doerr; Thomas Bäck (CoRR, 2020)

Squirrel: A Switching Hyperparameter Optimizer. - Noor H. Awad; Gresa Shala; Difan Deng; Neeratyoy Mallik; Matthias Feurer; Katharina Eggensperger; André Biedenkapp; Diederick Vermetten; Hao Wang 025; Carola Doerr; Marius Lindauer; Frank Hutter (CoRR, 2020)

OPTION: optimization algorithm benchmarking ontology. - Ana Kostovska; Diederick Vermetten; Carola Doerr; Saso Dzeroski; Pance Panov; Tome Eftimov (GECCO Companion, 2021)

Tuning as a means of assessing the benefits of new ideas in interplay with existing algorithmic modules. - Jacob de Nobel; Diederick Vermetten; Hao Wang 025; Carola Doerr; Thomas Bäck (GECCO Companion, 2021)

Is there anisotropy in structural bias? - Diederick Vermetten; Anna V. Kononova; Fabio Caraffini; Hao Wang 025; Thomas Bäck (GECCO Companion, 2021)

Benchmarking the Status of Default Pseudorandom Number Generators in Common Programming Languages. - Nils van den Honert; Diederick Vermetten; Anna V. Kononova (CoRR, 2021)

IOHanalyzer version 0.1.6.1 + example datasets. - Diederick Vermetten; Hao Wang 025; Furong Ye; Carola Doerr; Thomas Bäck (Zenodo, 2021)

Trajectory-based Algorithm Selection with Warm-starting. - Anja Jankovic 001; Diederick Vermetten; Ana Kostovska; Jacob de Nobel; Tome Eftimov; Carola Doerr (CEC, 2022)

Benchmarking and analyzing iterative optimization heuristics with IOH profiler. - Carola Doerr; Hao Wang 025; Diederick Vermetten; Thomas Bäck; Jacob de Nobel; Furong Ye (GECCO Companion, 2022)

The importance of landscape features for performance prediction of modular CMA-ES variants. - Ana Kostovska; Diederick Vermetten; Saso Dzeroski; Carola Doerr; Peter Korosec; Tome Eftimov (GECCO, 2022)

Using structural bias to analyse the behaviour of modular CMA-ES. - Diederick Vermetten; Fabio Caraffini; Bas van Stein; Anna V. Kononova (GECCO Companion, 2022)

Analyzing the impact of undersampling on the benchmarking and configuration of evolutionary algorithms. - Diederick Vermetten; Hao Wang 025; Manuel López-Ibáñez 001; Carola Doerr; Thomas Bäck (GECCO, 2022)

IOHanalyzer: Detailed performance analyses for iterative optimization heuristics: hot-off-the-press track @ GECCO 2022. - Hao Wang 025; Diederick Vermetten; Furong Ye; Carola Doerr; Thomas Bäck (GECCO Companion, 2022)

Per-run Algorithm Selection with Warm-Starting Using Trajectory-Based Features. - Ana Kostovska; Anja Jankovic 001; Diederick Vermetten; Jacob de Nobel; Hao Wang 025; Tome Eftimov; Carola Doerr (PPSN (1), 2022)

Non-elitist Selection Can Improve the Performance of Irace. - Furong Ye; Diederick Vermetten; Carola Doerr; Thomas Bäck (PPSN (1), 2022)

Non-Elitist Selection among Survivor Configurations can Improve the Performance of Irace. - Furong Ye; Diederick L. Vermetten; Carola Doerr; Thomas Bäck (CoRR, 2022)

Chaining of Numerical Black-box Algorithms: Warm-Starting and Switching Points. - Dominik Schröder; Diederick Vermetten; Hao Wang 025; Carola Doerr; Thomas Bäck (CoRR, 2022)

BIAS: A Toolbox for Benchmarking Structural Bias in the Continuous Domain. - Diederick Vermetten; Bas van Stein; Fabio Caraffini; Leandro L. Minku; Anna V. Kononova (IEEE Trans. Evol. Comput., 2022)

IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics. - Hao Wang 025; Diederick Vermetten; Furong Ye; Carola Doerr; Thomas Bäck (ACM Trans. Evol. Learn. Optim., 2022)

Analyzing the Impact of Undersampling on the Benchmarkingand Configuration of Evolutionary Algorithms - Dataset. - Diederick Vermetten; Hao Wang 025; Manuel López-Ibáñez 001; Carola Doerr; Thomas Bäck (Zenodo, 2022)

Linking Problem Landscape Features with the Performance of Individual CMA-ES Modules - Data. - Ana Kostovska; Diederick Vermetten; Saso Dzeroski; Carola Doerr; Peter Korosec; Tome Eftimov (Zenodo, 2022)

Per-Run Algorithm Selection with Warm-starting using Trajectory-based Features - Data. - Anja Jankovic 001; Ana Kostovska; Diederick Vermetten; Jacob de Nobel; Hao Wang 025; Tome Eftimov; Carola Doerr (Zenodo, 2022)

Data Sets for the study "Non-Elitist Selection Can Improve the Performance of Irace". - Furong Ye; Diederick Vermetten; Carola Doerr; Thomas Bäck (Zenodo, 2022)

To Switch or not to Switch: Predicting the Benefit of Switching between Algorithms based on Trajectory Features - Dataset. - Diederick Vermetten; Hao Wang 025; Kevin Sim; Emma Hart (Zenodo, 2022)

PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization. - Ana Kostovska; Gjorgjina Cenikj; Diederick Vermetten; Anja Jankovic 001; Ana Nikolikj; Urban Skvorc; Peter Korosec; Carola Doerr; Tome Eftimov (AutoML, 2023)

MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization Contexts. - Diederick Vermetten; Furong Ye; Thomas Bäck; Carola Doerr (AutoML, 2023)

Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler. - Frank Neumann 001; Aneta Neumann; Chao Qian 001; Anh Viet Do; Jacob de Nobel; Diederick Vermetten; Saba Sadeghi Ahouei; Furong Ye; Hao Wang 025; Thomas Bäck (CEC, 2023)

Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithms. - Ana Kostovska; Diederick Vermetten; Saso Dzeroski; Pance Panov; Tome Eftimov; Carola Doerr (EvoApplications@EvoStar, 2023)

BBOB Instance Analysis: Landscape Properties and Algorithm Performance Across Problem Instances. - Fu Xing Long; Diederick Vermetten; Bas van Stein; Anna V. Kononova (EvoApplications@EvoStar, 2023)

To Switch or Not to Switch: Predicting the Benefit of Switching Between Algorithms Based on Trajectory Features. - Diederick Vermetten; Hao Wang 025; Kevin Sim; Emma Hart (EvoApplications@EvoStar, 2023)

General Boolean Function Benchmark Suite. - Roman Kalkreuth; Zdenek Vasícek; Jakub Husa; Diederick Vermetten; Furong Ye; Thomas Bäck (FOGA, 2023)

Computing Star Discrepancies with Numerical Black-Box Optimization Algorithms. - François Clément; Diederick Vermetten; Jacob de Nobel; Alexandre D. Jesus; Luís Paquete; Carola Doerr (GECCO, 2023)

Benchmarking and analyzing iterative optimization heuristics with IOHprofiler. - Carola Doerr; Hao Wang 025; Diederick Vermetten; Thomas Bäck; Jacob de Nobel; Furong Ye (GECCO Companion, 2023)

Towards a General Boolean Function Benchmark Suite. - Roman Kalkreuth; Zdenek Vasícek; Jakub Husa; Diederick Vermetten; Furong Ye; Thomas Bäck (GECCO Companion, 2023)

Comparing Algorithm Selection Approaches on Black-Box Optimization Problems. - Ana Kostovska; Anja Jankovic 001; Diederick Vermetten; Saso Dzeroski; Tome Eftimov; Carola Doerr (GECCO Companion, 2023)

Assessing the Generalizability of a Performance Predictive Model. - Ana Nikolikj; Gjorgjina Cenikj; Gordana Ispirova; Diederick Vermetten; Ryan Dieter Lang; Andries Petrus Engelbrecht; Carola Doerr; Peter Korosec; Tome Eftimov (GECCO Companion, 2023)

Deep BIAS: Detecting Structural Bias using Explainable AI. - Bas van Stein; Diederick Vermetten; Fabio Caraffini; Anna V. Kononova (GECCO Companion, 2023)

When to be Discrete: Analyzing Algorithm Performance on Discretized Continuous Problems. - André Thomaser; Jacob de Nobel; Diederick Vermetten; Furong Ye; Thomas Bäck; Anna V. Kononova (GECCO, 2023)

Analysis of modular CMA-ES on strict box-constrained problems in the SBOX-COST benchmarking suite. - Diederick Vermetten; Manuel López-Ibáñez 001; Olaf Mersmann; Richard Allmendinger 001; Anna V. Kononova (GECCO Companion, 2023)

Modular Differential Evolution. - Diederick Vermetten; Fabio Caraffini; Anna V. Kononova; Thomas Bäck (GECCO, 2023)

Using Affine Combinations of BBOB Problems for Performance Assessment. - Diederick Vermetten; Furong Ye; Carola Doerr (GECCO, 2023)

Challenges of ELA-Guided Function Evolution Using Genetic Programming. - Fu Xing Long; Diederick Vermetten; Anna V. Kononova; Roman Kalkreuth; Kaifeng Yang; Thomas Bäck; Niki van Stein (IJCCI, 2023)

Synergizing Theory and Practice of Automated Algorithm Design for Optimization (Dagstuhl Seminar 23332). - Diederick Vermetten; Martin S. Krejca; Marius Lindauer; Manuel López-Ibáñez 001; Katherine M. Malan (Dagstuhl Reports, 2023)

Evolutionary Algorithms for Parameter Optimization - Thirty Years Later. - Thomas H. W. Bäck; Anna V. Kononova; Bas van Stein; Hao Wang 025; Kirill A. Antonov; Roman T. Kalkreuth; Jacob de Nobel; Diederick Vermetten; Roy de Winter; Furong Ye (Evol. Comput., 2023)

Computing Star Discrepancies with Numerical Black-Box Optimization Algorithms - Code and Data. - François Clément; Diederick Vermetten; Jacob de Nobel; Alexandre D. Jesus; Luís Paquete; Carola Doerr (Zenodo, 2023)

Using Affine Combinations of BBOB Problems for Performance Assessment - Code and Data. - Diederick Vermetten; Furong Ye; Carola Doerr (Zenodo, 2023)

Challenges of ELA-based Function Evolution using Genetic Programming - Reproducability files. - Fu Xing Long; Diederick Vermetten; Anna V. Kononova; Roman Kalkreuth; Kaifeng Yang; Thomas Bäck; Niki van Stein (Zenodo, 2023)

MA-BBOB - Reproducibility and Additional Data. - Diederick Vermetten; Furong Ye; Thomas Bäck; Carola Doerr (Zenodo, 2023)

Quantifying Individual and Joint Module Impact in Modular Optimization Frameworks. - Ana Nikolikj; Ana Kostovska; Diederick Vermetten; Carola Doerr; Tome Eftimov (CEC, 2024)

Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization. - Konstantin Dietrich; Diederick Vermetten; Carola Doerr; Pascal Kerschke (GECCO, 2024)

Benchmarking and Analyzing Iterative Optimization Heuristics with IOHprofiler. - Carola Doerr; Diederick Vermetten; Jacob de Nobel; Thomas Bäck (GECCO Companion, 2024)

A Critical Analysis of Raven Roost Optimization. - Martijn Halsema; Diederick Vermetten; Thomas Bäck; Niki van Stein (GECCO Companion, 2024)

Transfer Learning of Surrogate Models via Domain Affine Transformation. - Shuaiqun Pan; Diederick Vermetten; Manuel López-Ibáñez 001; Thomas Bäck; Hao Wang 025 (GECCO, 2024)

Large-Scale Benchmarking of Metaphor-Based Optimization Heuristics. - Diederick Vermetten; Carola Doerr; Hao Wang 025; Anna V. Kononova; Thomas Bäck (GECCO, 2024)

Sampling in CMA-ES: Low Numbers of Low Discrepancy Points. - Jacob de Nobel; Diederick Vermetten; Thomas H. W. Bäck; Anna V. Kononova (IJCCI, 2024)

Impact of Spatial Transformations on Exploratory and Deep-Learning Based Landscape Features of CEC2022 Benchmark Suite. - Haoran Yin; Diederick Vermetten; Furong Ye; Thomas H. W. Bäck; Anna V. Kononova (IJCCI, 2024)

Optimizing Causal Interventions in Hybrid Bayesian Networks - A Discretization, Knowledge Compilation, and Heuristic Optimization Approach. - Maarten C. Vonk; Diederick Vermetten; Jacob de Nobel; Sebastiaan Brand; Ninoslav Malekovic; Thomas Bäck; Alfons Laarman; Anna V. Kononova (IPMU (1), 2024)

Avoiding Redundant Restarts in Multimodal Global Optimization. - Jacob de Nobel; Diederick Vermetten; Anna V. Kononova; Ofer M. Shir; Thomas Bäck (PPSN (2), 2024)

Empirical Analysis of the Dynamic Binary Value Problem with IOHprofiler. - Diederick Vermetten; Johannes Lengler; Dimitri Rusin; Thomas Bäck; Carola Doerr (PPSN (2), 2024)

Impact of spatial transformations on landscape features of CEC2022 basic benchmark problems. - Haoran Yin; Diederick Vermetten; Furong Ye; Thomas H. W. Bäck; Anna V. Kononova (CoRR, 2024)

In-the-loop Hyper-Parameter Optimization for LLM-Based Automated Design of Heuristics. - Niki van Stein; Diederick Vermetten; Thomas Bäck (CoRR, 2024)

MO-IOHinspector: Anytime Benchmarking of Multi-Objective Algorithms using IOHprofiler. - Diederick Vermetten; Jeroen Rook; Oliver Ludger Preuß; Jacob de Nobel; Carola Doerr; Manuel López-Ibáñez 001; Heike Trautmann; Thomas Bäck (CoRR, 2024)

The Importance of Being Constrained: Dealing with Infeasible Solutions in Differential Evolution and Beyond. - Anna V. Kononova; Diederick Vermetten; Fabio Caraffini; Madalina-Andreea Mitran; Daniela Zaharie (Evol. Comput., 2024)

IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics. - Jacob de Nobel; Furong Ye; Diederick Vermetten; Hao Wang 025; Carola Doerr; Thomas Bäck (Evol. Comput., 2024)

Large-Scale Benchmarking of Metaphor-Based Optimization Heuristics - Reproducibility Files. - Diederick Vermetten; Carola Doerr; Hao Wang 025; Anna V. Kononova; Thomas Bäck (Zenodo, 2024)

Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization - Reproducibility Files. - Konstantin Dietrich; Diederick Vermetten; Carola Doerr; Pascal Kerschke (Zenodo, 2024)

Benchmarking Dynamic Binary Value Problems with IOHprofiler - Reproducibility files. - Diederick Vermetten; Johannes Lengler; Dimitri Rusin; Thomas Bäck; Carola Doerr (Zenodo, 2024)

MA-BBOB - Reproducibility and Additional Data. - Diederick Vermetten; Furong Ye; Thomas Bäck; Carola Doerr (Zenodo, 2024)

MA-BBOB - Reproducibility and Additional Data. - Diederick Vermetten; Furong Ye; Thomas Bäck; Carola Doerr (Zenodo, 2024)

Sampling in CMA-ES: Low Numbers of Low Discrepancy Points. - Jacob de Nobel; Diederick Vermetten; Anna V. Kononova; Thomas Bäck (Zenodo, 2024)

IOH-Xplainer - code, experiments and results. - Niki van Stein; Diederick Vermetten; Anna V. Kononova; Thomas Bäck (Zenodo, 2024)

Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks. - Shuaiqun Pan; Diederick Vermetten; Manuel López-Ibáñez 001; Thomas Bäck; Hao Wang 025 (CEC, 2025)

MO-IOHinspector: Anytime Benchmarking of Multi-objective Algorithms Using IOHprofiler. - Diederick Vermetten; Jeroen Rook; Oliver L. Preuß; Jacob de Nobel; Carola Doerr; Manuel López-Ibáñez 001; Heike Trautmann; Thomas Bäck (EMO (1), 2025)

Stalling in Space: Attractor Analysis for Any Algorithm. - Sarah L. Thomson; Quentin Renau; Diederick Vermetten; Emma Hart; Niki van Stein; Anna V. Kononova (EvoApplications (2), 2025)

A Standardized Benchmark Set of Clustering Problem Instances for Comparing Black-Box Optimizers. - Diederick Vermetten; Catalin-Viorel Dinu; Marcus Gallagher (FOGA, 2025)

Abnormal Mutations: Evolution Strategies Don't Require Gaussianity. - Jacob de Nobel; Diederick Vermetten; Hao Wang 025; Anna V. Kononova; Günter Rudolph; Thomas Bäck (GECCO, 2025)

MA-BBOB: A Problem Generator for Black-Box Optimization Using Affine Combinations and Shifts. - Diederick Vermetten; Furong Ye; Thomas Bäck; Carola Doerr (ACM Trans. Evol. Learn. Optim., 2025)

Transfer Learning of Surrogate Models: Integrating Domain Warping and Affine Transformations. - Shuaiqun Pan; Diederick Vermetten; Manuel López-Ibáñez 001; Thomas Bäck; Hao Wang 025 (CoRR, 2025)

Transfer Learning of Surrogate Models: Integrating Domain Warping and Affine Transformations (Supplementary Material). - Shuaiqun Pan; Diederick Vermetten; Manuel López-Ibáñez 001; Thomas Bäck; Hao Wang 025 (Zenodo, 2025)

Explainable Benchmarking for Iterative Optimization Heuristics. - Niki van Stein; Diederick Vermetten; Anna V. Kononova; Thomas Bäck (ACM Trans. Evol. Learn. Optim., 2025)

Using the Empirical Attainment Function for Analyzing Single-Objective Black-Box Optimization Algorithms. - Manuel López-Ibáñez 001; Diederick Vermetten; Johann Dréo; Carola Doerr (IEEE Trans. Evol. Comput., 2025)