1 code implementation • 16 May 2024 • Christian Internò, Elena Raponi, Niki van Stein, Thomas Bäck, Markus Olhofer, Yaochu Jin, Barbara Hammer
However, its application on edge devices is hindered by limited computational capabilities and data communication challenges, compounded by the inherent complexity of Deep Learning (DL) models.
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.