no code implementations • 20 Dec 2024 • Sarah L. Thomson, Quentin Renau, Diederick Vermetten, Emma Hart, Niki van Stein, Anna V. Kononova
However, STNs are not typically modelled in such a way that models temporal stalls: that is, a region in the search space where an algorithm fails to find a better solution over a defined period of time.
no code implementations • 4 Dec 2024 • Haoran Yin, Anna V. Kononova, Thomas Bäck, Niki van Stein
Experiments using GPT-3. 5-turbo and GPT-4o models demonstrate that GPT-3. 5-turbo fails to adhere to the specific mutation instructions, while GPT-4o is able to adapt its mutation based on the prompt engineered dynamic prompts.
1 code implementation • 29 Oct 2024 • Koen Ponse, Aske Plaat, Niki van Stein, Thomas M. Moerland
Accurate economic simulations often require many experimental runs, particularly when combined with reinforcement learning.
no code implementations • 17 Oct 2024 • Kirill Antonov, Marijn Siemons, Niki van Stein, Thomas H. W. Bäck, Ralf Kohlhaas, Anna V. Kononova
This work addresses the critical challenge of optimal filter selection for a novel trace gas measurement device.
no code implementations • 7 Oct 2024 • 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.
no code implementations • 14 Sep 2024 • Qi Huang, Sofoklis Kitharidis, Thomas Bäck, Niki van Stein
In time-series classification, understanding model decisions is crucial for their application in high-stakes domains such as healthcare and finance.
no code implementations • 2 Sep 2024 • Fu Xing Long, Moritz Frenzel, Peter Krause, Markus Gitterle, Thomas Bäck, Niki van Stein
Overall, configurations with better performance can be best identified by using NN models trained on a combination of RGF and MA-BBOB functions.
no code implementations • 17 Jul 2024 • Qi Huang, Emanuele Mezzi, Osman Mutlu, Miltiadis Kofinas, Vidya Prasad, Shadnan Azwad Khan, Elena Ranguelova, Niki van Stein
We introduce a novel metric for measuring semantic continuity in Explainable AI methods and machine learning models.
no code implementations • 16 Jul 2024 • Aske Plaat, Annie Wong, Suzan Verberne, Joost Broekens, Niki van Stein, Thomas Back
The field started with the question whether LLMs can solve grade school math word problems.
no code implementations • 12 Jun 2024 • Ryan Zhou, Jaume Bacardit, Alexander Brownlee, Stefano Cagnoni, Martin Fyvie, Giovanni Iacca, John McCall, Niki van Stein, David Walker, Ting Hu
Artificial intelligence methods are being increasingly applied across various domains, but their often opaque nature has raised concerns about accountability and trust.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
no code implementations • 8 Jun 2024 • Gjorgjina Cenikj, Ana Nikolikj, Gašper Petelin, Niki van Stein, Carola Doerr, Tome Eftimov
The selection of the most appropriate algorithm to solve a given problem instance, known as algorithm selection, is driven by the potential to capitalize on the complementary performance of different algorithms across sets of problem instances.
1 code implementation • 30 May 2024 • Niki van Stein, Thomas Bäck
This paper introduces a novel Large Language Model Evolutionary Algorithm (LLaMEA) framework, leveraging GPT models for the automated generation and refinement of algorithms.
1 code implementation • 16 May 2024 • Christian Internò, Elena Raponi, Niki van Stein, Thomas Bäck, Markus Olhofer, Yaochu Jin, Barbara Hammer
The rapid proliferation of smart devices coupled with the advent of 6G networks has profoundly reshaped the domain of collaborative machine learning.
no code implementations • 26 Apr 2024 • 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 a wide variety of search landscapes.
no code implementations • 9 Feb 2024 • Kirill Antonov, Roman Kalkreuth, Kaifeng Yang, Thomas Bäck, Niki van Stein, Anna V Kononova
The superior performance of the proposed algorithm and insights into the limitations of GP open the way for further advancing GP for SR and related areas of explainable machine learning.
no code implementations • 2 Feb 2024 • Qi Huang, Wei Chen, Thomas Bäck, Niki van Stein
In this work, we propose a model-agnostic instance-based post-hoc explainability method for time series classification.
1 code implementation • 31 Jan 2024 • Niki van Stein, Diederick Vermetten, Anna V. Kononova, Thomas Bäck
Introducing the IOH-Xplainer software framework, for analyzing and understanding the performance of various optimization algorithms and the impact of their different components and hyper-parameters.
no code implementations • 21 Sep 2023 • Christiaan Lamers, Rene Vidal, Nabil Belbachir, Niki van Stein, Thomas Baeck, Paris Giampouras
A key challenge in this setting is the so-called "catastrophic forgetting problem", in which the performance of the learner in an "old task" decreases when subsequently trained on a "new task".
no code implementations • 5 Jun 2023 • Kirill Antonov, Anna V. Kononova, Thomas Bäck, Niki van Stein
Locality is a crucial property for efficiently optimising black-box problems with randomized search heuristics.
no code implementations • 24 May 2023 • Fu Xing Long, Diederick Vermetten, Anna V. Kononova, Roman Kalkreuth, Kaifeng Yang, Thomas Bäck, Niki van Stein
Within the optimization community, the question of how to generate new optimization problems has been gaining traction in recent years.