The intersection of evolutionary computation and explainable AI
In the past decade, Explainable Artificial Intelligence (XAI) has attracted a great interest in
the research community, motivated by the need for explanations in critical AI applications …
the research community, motivated by the need for explanations in critical AI applications …
Adaptive local landscape feature vector for problem classification and algorithm selection
Fitness landscape analysis is a data-driven technique to study the relationship between
problem characteristics and algorithm performance by characterizing the landscape features …
problem characteristics and algorithm performance by characterizing the landscape features …
Explainable benchmarking for iterative optimization heuristics
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 heuristic …
what kind of problems certain algorithms perform well. In most current research into heuristic …
Using affine combinations of bbob problems for performance assessment
Benchmarking plays a major role in the development and analysis of optimization
algorithms. As such, the way in which the used benchmark problems are defined …
algorithms. As such, the way in which the used benchmark problems are defined …
Explainable landscape analysis in automated algorithm performance prediction
R Trajanov, S Dimeski, M Popovski, P Korošec… - … Conference on the …, 2022 - Springer
Predicting the performance of an optimization algorithm on a new problem instance is
crucial in order to select the most appropriate algorithm for solving that problem instance …
crucial in order to select the most appropriate algorithm for solving that problem instance …
From fitness landscapes to explainable AI and back
We consider and discuss the ways in which search landscapes might contribute to the future
of explainable artificial intelligence (XAI), and vice versa. Landscapes are typically used to …
of explainable artificial intelligence (XAI), and vice versa. Landscapes are typically used to …
The importance of landscape features for performance prediction of modular CMA-ES variants
Selecting the most suitable algorithm and determining its hyperparameters for a given
optimization problem is a challenging task. Accurately predicting how well a certain …
optimization problem is a challenging task. Accurately predicting how well a certain …
Identifying minimal set of exploratory landscape analysis features for reliable algorithm performance prediction
A Nikolikj, R Trajanov, G Cenikj… - 2022 IEEE Congress …, 2022 - ieeexplore.ieee.org
Exploratory Landscape Analysis (ELA) enables the characterization of black-box
optimization problem instances in the form of numerical features. Such features can be used …
optimization problem instances in the form of numerical features. Such features can be used …
Using LLM for automatic evolvement of metaheuristics from swarm algorithm SOMA
M Pluhacek, J Kovac, A Viktorin, P Janku… - Proceedings of the …, 2024 - dl.acm.org
This study investigates the use of the GPT-4 Turbo, a large language model, to enhance the
Self-Organizing Migrating Algorithm (SOMA), specifically its All to All variant (SOMA-ATA) …
Self-Organizing Migrating Algorithm (SOMA), specifically its All to All variant (SOMA-ATA) …
Algorithm Instance Footprint: Separating Easily Solvable and Challenging Problem Instances
In black-box optimization, it is essential to understand why an algorithm instance works on a
set of problem instances while failing on others and provide explanations of its behavior. We …
set of problem instances while failing on others and provide explanations of its behavior. We …