104 citations · 367 across the 54 of their papers we have counts for
63 papers
The Modular CMA-ES: A Framework for Modern Evolution Strategies
Jacob de Nobel, Diederick Vermetten, Anna V. Kononova +2
Since their introduction, modern evolution strategies, such as the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), have become established as powerful methods for continu…
EvoMO-SR: Multiobjective LLM-based Evolution of Symbolic Expressions with substructure guidance
Cristina Rossetti, Anna V. Kononova, Thomas Bäck +2
Symbolic Regression (SR) is a data-driven method for scientific discovery which searches for interpretable analytical relationships within data. Recently, Large Language Models (LL…
DistributedDesignOptimizer: A modular Python framework for Setup, Execution and Processing of Distributed Design Optimization
Sebastian Ellmaier, Albert J. de Wit, Akilesh Raveendran +3
Multidisciplinary Design Optimization (MDO) enables the application of optimization algorithms to complex engineered systems, ranging from aerospace and automotive to robotics and…
Benefits of Low-Cost Bio-Inspiration in the Age of Overparametrization
Kevin Godin-Dubois, Anil Yaman, Anna V. Kononova
While Central Pattern Generators (CPGs) and Multi-Layer Perceptrons (MLP) are widely used paradigms in robot control, few systematic studies have been performed on the relative mer…
Multi-Agent Influence Diagrams to Hybrid Threat Modeling
Maarten C. Vonk, Anna V. Kononova, Thomas Bäck +1
Western governments have adopted an assortment of counter-hybrid threat measures to defend against hostile actions below the conventional military threshold. The impact of these me…
Structural bias in multi-objective optimisation
Jakub Kudela, Niki van Stein, Thomas Bäck +1
Structural bias (SB) refers to systematic preferences of an optimisation algorithm for particular regions of the search space that arise independently of the objective function. Wh…
Landscape-aware Automated Algorithm Design: An Efficient Framework for Real-world Optimization
Haoran Yin, Shuaiqun Pan, Zhao Wei +5
The advent of Large Language Models (LLMs) has opened new frontiers in automated algorithm design, giving rise to numerous powerful methods. However, these approaches retain critic…
Lens-descriptor guided evolutionary algorithm for optimization of complex optical systems with glass choice
Kirill Antonov, Teus Tukker, Tiago Botari +3
Designing high-performance optical lenses entails exploring a high-dimensional, tightly constrained space of surface curvatures, glass choices, element thicknesses, and spacings. I…
LLaMEA-SAGE: Guiding Automated Algorithm Design with Structural Feedback from Explainable AI
Niki van Stein, Anna V. Kononova, Lars Kotthoff +1
Large language models have enabled automated algorithm design (AAD) by generating optimization algorithms directly from natural-language prompts. While evolutionary frameworks such…
State Design Matters: How Representations Shape Dynamic Reasoning in Large Language Models
Annie Wong, Aske Plaat, Thomas Bäck +2
As large language models (LLMs) move from static reasoning tasks toward dynamic environments, their success depends on the ability to navigate and respond to an environment that ch…
How does downsampling affect needle electromyography signals? A generalisable workflow for understanding downsampling effects on high-frequency time series
Mathieu Cherpitel, Janne Luijten, Thomas Bäck +3
Automated analysis of needle electromyography (nEMG) signals is emerging as a tool to support the detection of neuromuscular diseases (NMDs), yet the signals' high and heterogeneou…
From Performance to Understanding: A Vision for Explainable Automated Algorithm Design
Niki van Stein, Anna V. Kononova, Thomas Bäck
Automated algorithm design is entering a new phase: Large Language Models can now generate full optimisation (meta)heuristics, explore vast design spaces and adapt through iterativ…
Benchmarking that Matters: Rethinking Benchmarking for Practical Impact
Anna V. Kononova, Niki van Stein, Olaf Mersmann +14
Benchmarking has driven scientific progress in Evolutionary Computation, yet current practices fall short of real-world needs. Widely used synthetic suites such as BBOB and CEC iso…
Lost in Vagueness: Towards Context-Sensitive Standards for Robustness Assessment under the EU AI Act
Roberta Tamponi, Carina Prunkl, Thomas Bäck +1
Robustness is a key requirement for high-risk AI systems under the EU Artificial Intelligence Act (AI Act). However, both its definition and assessment methods remain underspecifie…
Design of efficient high-order immersed metagratings using an evolutionary algorithm
Dhwanil Patel, Jacob de Nobel, Anna V. Kononova +2
Immersed reflection gratings improve spectral resolving power by enabling diffraction within a high refractive index medium. This principle has been widely adopted to make grating…
DQS: A Low-Budget Query Strategy for Enhancing Unsupervised Data-driven Anomaly Detection Approaches
Lucas Correia, Jan-Christoph Goos, Thomas Bäck +1
Truly unsupervised approaches for time series anomaly detection are rare in the literature. Those that exist suffer from a poorly set threshold, which hampers detection performance…
Behaviour Space Analysis of LLM-driven Meta-heuristic Discovery
Niki van Stein, Haoran Yin, Anna V. Kononova +2
We investigate the behaviour space of meta-heuristic optimisation algorithms automatically generated by Large Language Model driven algorithm discovery methods. Using the Large Lan…
From Spikes to Speech: NeuroVoc -- A Biologically Plausible Vocoder Framework for Auditory Perception and Cochlear Implant Simulation
Jacob de Nobel, Jeroen J. Briaire, Thomas H. W. Baeck +2
We present NeuroVoc, a flexible model-agnostic vocoder framework that reconstructs acoustic waveforms from simulated neural activity patterns using an inverse Fourier transform. Th…
Reasoning Capabilities of Large Language Models on Dynamic Tasks
Annie Wong, Thomas Bäck, Aske Plaat +2
Large language models excel on static benchmarks, but their ability as self-learning agents in dynamic environments remains unclear. We evaluate three prompting strategies: self-re…
BLADE: Benchmark suite for LLM-driven Automated Design and Evolution of iterative optimisation heuristics
Niki van Stein, Anna V. Kononova, Haoran Yin +1
The application of Large Language Models (LLMs) for Automated Algorithm Discovery (AAD), particularly for optimisation heuristics, is an emerging field of research. This emergence…
Graphical Models for Decision-Making: Integrating Causality and Game Theory
Maarten C. Vonk, Mauricio Gonzalez Soto, Anna V. Kononova
Causality and game theory are two influential fields that contribute significantly to decision-making in various domains. Causality defines and models causal relationships in compl…
Optimizing Photonic Structures with Large Language Model Driven Algorithm Discovery
Haoran Yin, Anna V. Kononova, Thomas Bäck +1
We study how large language models can be used in combination with evolutionary computation techniques to automatically discover optimization algorithms for the design of photonic…
Code Evolution Graphs: Understanding Large Language Model Driven Design of Algorithms
Niki van Stein, Anna V. Kononova, Lars Kotthoff +1
Large Language Models (LLMs) have demonstrated great promise in generating code, especially when used inside an evolutionary computation framework to iteratively optimize the gener…
Abnormal Mutations: Evolution Strategies Don't Require Gaussianity
Jacob de Nobel, Diederick Vermetten, Hao Wang +3
The mutation process in evolution strategies has been interlinked with the normal distribution since its inception. Many lines of reasoning have been given for this strong dependen…