59 citations · 144 across the 61 of their papers we have counts for
18 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…
Controlling the Mutation in Large Language Models for the Efficient Evolution of Algorithms
Haoran Yin, Anna V. Kononova, Thomas Bäck +1
The integration of Large Language Models (LLMs) with evolutionary computation (EC) has introduced a promising paradigm for automating the design of metaheuristic algorithms. Howeve…
Development of a photonic crystal spectrometer for greenhouse gas measurements
Marijn Siemonsa, Martijn Veen, Irina Malysheva +9
The need of atmospheric information with a higher spatial and temporal resolution drives the development of small satellites and satellite constellations to complement satellite fl…
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…
EconoJax: A Fast & Scalable Economic Simulation in Jax
Koen Ponse, Aske Plaat, Niki van Stein +1
Accurate economic simulations often require many experimental runs, particularly when combined with reinforcement learning. Unfortunately, training reinforcement learning agents in…
Selection of Filters for Photonic Crystal Spectrometer Using Domain-Aware Evolutionary Algorithms
Kirill Antonov, Marijn Siemons, Niki van Stein +3
This work addresses the critical challenge of optimal filter selection for a novel trace gas measurement device. This device uses photonic crystal filters to retrieve trace gas con…
Landscape-Aware Automated Algorithm Configuration using Multi-output Mixed Regression and Classification
Fu Xing Long, Moritz Frenzel, Peter Krause +3
In landscape-aware algorithm selection problem, the effectiveness of feature-based predictive models strongly depends on the representativeness of training data for practical appli…
TX-Gen: Multi-Objective Optimization for Sparse Counterfactual Explanations for Time-Series Classification
Qi Huang, Sofoklis Kitharidis, Thomas Bäck +1
In time-series classification, understanding model decisions is crucial for their application in high-stakes domains such as healthcare and finance. Counterfactual explanations, wh…
Beyond the Veil of Similarity: Quantifying Semantic Continuity in Explainable AI
Qi Huang, Emanuele Mezzi, Osman Mutlu +5
We introduce a novel metric for measuring semantic continuity in Explainable AI methods and machine learning models. We posit that for models to be truly interpretable and trustwor…
Multi-Step Reasoning with Large Language Models, a Survey
Aske Plaat, Annie Wong, Suzan Verberne +3
Large language models (LLMs) with billions of parameters exhibit in-context learning abilities, enabling few-shot learning on tasks that the model was not specifically trained for.…
Evolutionary Computation and Explainable AI: A Roadmap to Understandable Intelligent Systems
Ryan Zhou, Jaume Bacardit, Alexander Brownlee +7
Artificial intelligence methods are being increasingly applied across various domains, but their often opaque nature has raised concerns about accountability and trust. In response…
A Survey of Features Used for Representing Black-box Single-objective Continuous Optimization
Gjorgjina Cenikj, Ana Nikolikj, Gašper Petelin +3
This survey examines key advancements in designing features to represent optimization problem instances, algorithm instances, and their interactions within the context of single-ob…
LLaMEA: A Large Language Model Evolutionary Algorithm for Automatically Generating Metaheuristics
Niki van Stein, Thomas Bäck
Large Language Models (LLMs) such as GPT-4 have demonstrated their ability to understand natural language and generate complex code snippets. This paper introduces a novel Large La…
Pruning Federated Models through Loss Landscape Analysis and Client Agreement Scoring
Christian Internò, Elena Raponi, Markus Olhofer +5
The practical deployment of Federated Learning (FL) on resource-constrained devices is fundamentally limited by the high cost of training large models and the instability caused by…
A Deep Dive into Effects of Structural Bias on CMA-ES Performance along Affine Trajectories
Niki van Stein, Sarah L. Thomson, Anna V. Kononova
To guide the design of better iterative optimisation heuristics, it is imperative to understand how inherent structural biases within algorithm components affect the performance on…
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…
A Functional Analysis Approach to Symbolic Regression
Kirill Antonov, Roman Kalkreuth, Kaifeng Yang +3
Symbolic regression (SR) poses a significant challenge for randomized search heuristics due to its reliance on the synthesis of expressions for input-output mappings. Although trad…
Shapelet-based Model-agnostic Counterfactual Local Explanations for Time Series Classification
Qi Huang, Wei Chen, Thomas Bäck +1
In this work, we propose a model-agnostic instance-based post-hoc explainability method for time series classification. The proposed algorithm, namely Time-CF, leverages shapelets…