109 citations · 277 across the 52 of their papers we have counts for
15 papers · 1 filter
MA-BBOB: A Problem Generator for Black-Box Optimization Using Affine Combinations and Shifts
Diederick Vermetten, Furong Ye, Thomas Bäck +1
Choosing a set of benchmark problems is often a key component of any empirical evaluation of iterative optimization heuristics. In continuous, single-objective optimization, severa…
PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization
Ana Kostovska, Gjorgjina Cenikj, Diederick Vermetten +6
The performance of automated algorithm selection (AAS) strongly depends on the portfolio of algorithms to choose from. Selecting the portfolio is a non-trivial task that requires b…
Comparing Algorithm Selection Approaches on Black-Box Optimization Problems
Ana Kostovska, Anja Jankovic, Diederick Vermetten +3
Performance complementarity of solvers available to tackle black-box optimization problems gives rise to the important task of algorithm selection (AS). Automated AS approaches can…
Computing Star Discrepancies with Numerical Black-Box Optimization Algorithms
François Clément, Diederick Vermetten, Jacob de Nobel +3
The star discrepancy is a measure for the regularity of a finite set of points taken from . Low discrepancy point sets are highly relevant for Quasi-Monte Car…
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 +1
Extending a recent suggestion to generate new instances for numerical black-box optimization benchmarking by interpolating pairs of the well-established BBOB functions from the COm…
Assessing the Generalizability of a Performance Predictive Model
Ana Nikolikj, Gjorgjina Cenikj, Gordana Ispirova +6
A key component of automated algorithm selection and configuration, which in most cases are performed using supervised machine learning (ML) methods is a good-performing predictive…
Challenges of ELA-guided Function Evolution using Genetic Programming
Fu Xing Long, Diederick Vermetten, Anna V. Kononova +4
Within the optimization community, the question of how to generate new optimization problems has been gaining traction in recent years. Within topics such as instance space analysi…
Analysis of modular CMA-ES on strict box-constrained problems in the SBOX-COST benchmarking suite
Diederick Vermetten, Manuel López-Ibáñez, Olaf Mersmann +2
Box-constraints limit the domain of decision variables and are common in real-world optimization problems, for example, due to physical, natural or spatial limitations. Consequentl…
When to be Discrete: Analyzing Algorithm Performance on Discretized Continuous Problems
André Thomaser, Jacob de Nobel, Diederick Vermetten +3
The domain of an optimization problem is seen as one of its most important characteristics. In particular, the distinction between continuous and discrete optimization is rather im…
Modular Differential Evolution
Diederick Vermetten, Fabio Caraffini, Anna V. Kononova +1
New contributions in the field of iterative optimisation heuristics are often made in an iterative manner. Novel algorithmic ideas are not proposed in isolation, but usually as an…
Deep-BIAS: Detecting Structural Bias using Explainable AI
Bas van Stein, Diederick Vermetten, Fabio Caraffini +1
Evaluating the performance of heuristic optimisation algorithms is essential to determine how well they perform under various conditions. Recently, the BIAS toolbox was introduced…
Using Affine Combinations of BBOB Problems for Performance Assessment
Diederick Vermetten, Furong Ye, Carola Doerr
Benchmarking plays a major role in the development and analysis of optimization algorithms. As such, the way in which the used benchmark problems are defined significantly affects…
To Switch or not to Switch: Predicting the Benefit of Switching between Algorithms based on Trajectory Features
Diederick Vermetten, Hao Wang, Kevin Sim +1
Dynamic algorithm selection aims to exploit the complementarity of multiple optimization algorithms by switching between them during the search. While these kinds of dynamic algori…
Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler
Frank Neumann, Aneta Neumann, Chao Qian +7
Submodular functions play a key role in the area of optimization as they allow to model many real-world problems that face diminishing returns. Evolutionary algorithms have been sh…
Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithms
Ana Kostovska, Diederick Vermetten, Sašo Džeroski +3
Empirical data plays an important role in evolutionary computation research. To make better use of the available data, ontologies have been proposed in the literature to organize t…