109 citations · 277 across the 52 of their papers we have counts for
12 papers · 1 filter
Stalling in Space: Attractor Analysis for any Algorithm
Sarah L. Thomson, Quentin Renau, Diederick Vermetten +3
Network-based representations of fitness landscapes have grown in popularity in the past decade; this is probably because of growing interest in explainability for optimisation alg…
MO-IOHinspector: Anytime Benchmarking of Multi-Objective Algorithms using IOHprofiler
Diederick Vermetten, Jeroen Rook, Oliver L. Preuß +5
Benchmarking is one of the key ways in which we can gain insight into the strengths and weaknesses of optimization algorithms. In sampling-based optimization, considering the anyti…
In-the-loop Hyper-Parameter Optimization for LLM-Based Automated Design of Heuristics
Niki van Stein, Diederick Vermetten, Thomas Bäck
Large Language Models (LLMs) have shown great potential in automatically generating and optimizing (meta)heuristics, making them valuable tools in heuristic optimization tasks. How…
Sampling in CMA-ES: Low Numbers of Low Discrepancy Points
Jacob de Nobel, Diederick Vermetten, Thomas H. W. Bäck +1
The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is one of the most successful examples of a derandomized evolution strategy. However, it still relies on randomly sampl…
Quantifying Individual and Joint Module Impact in Modular Optimization Frameworks
Ana Nikolikj, Ana Kostovska, Diederick Vermetten +2
This study explores the influence of modules on the performance of modular optimization frameworks for continuous single-objective black-box optimization. There is an extensive var…
Avoiding Redundant Restarts in Multimodal Global Optimization
Jacob de Nobel, Diederick Vermetten, Anna V. Kononova +2
Naïve restarts of global optimization solvers when operating on multimodal search landscapes may resemble the Coupon's Collector Problem, with a potential to waste significant func…
Empirical Analysis of the Dynamic Binary Value Problem with IOHprofiler
Diederick Vermetten, Johannes Lengler, Dimitri Rusin +2
Optimization problems in dynamic environments have recently been the source of several theoretical studies. One of these problems is the monotonic Dynamic Binary Value problem, whi…
Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization
Konstantin Dietrich, Diederick Vermetten, Carola Doerr +1
The recently proposed MA-BBOB function generator provides a way to create numerical black-box benchmark problems based on the well-established BBOB suite. Initial studies on this g…
Using the Empirical Attainment Function for Analyzing Single-objective Black-box Optimization Algorithms
Manuel López-Ibáñez, Diederick Vermetten, Johann Dreo +1
A widely accepted way to assess the performance of iterative black-box optimizers is to analyze their empirical cumulative distribution function (ECDF) of pre-defined quality targe…
Explainable Benchmarking for Iterative Optimization Heuristics
Niki van Stein, Diederick Vermetten, Anna V. Kononova +1
Benchmarking heuristic algorithms is vital to understand under which conditions and on what kind of problems certain algorithms perform well. In most current research into heuristi…
Large-scale Benchmarking of Metaphor-based Optimization Heuristics
Diederick Vermetten, Carola Doerr, Hao Wang +2
The number of proposed iterative optimization heuristics is growing steadily, and with this growth, there have been many points of discussion within the wider community. One partic…
Impact of spatial transformations on landscape features of CEC2022 basic benchmark problems
Haoran Yin, Diederick Vermetten, Furong Ye +2
When benchmarking optimization heuristics, we need to take care to avoid an algorithm exploiting biases in the construction of the used problems. One way in which this might be don…