2 citations · 2 across the 5 of their papers we have counts for
5 papers
Class Incremental Learning for Algorithm Selection
Mate Botond Nemeth, Emma Hart, Kevin Sim +1
Algorithm selection is commonly used to predict the best solver from a portfolio per per-instance. In many real scenarios, instances arrive in a stream: new instances become availa…
Beyond the Hype: Benchmarking LLM-Evolved Heuristics for Bin Packing
Kevin Sim, Quentin Renau, Emma Hart
Coupling Large Language Models (LLMs) with Evolutionary Algorithms has recently shown significant promise as a technique to design new heuristics that outperform existing methods,…
Evaluating the Robustness of Deep-Learning Algorithm-Selection Models by Evolving Adversarial Instances
Emma Hart, Quentin Renau, Kevin Sim +1
Deep neural networks (DNN) are increasingly being used to perform algorithm-selection in combinatorial optimisation domains, particularly as they accommodate input representations…
To Switch or not to Switch: Predicting the Benefit of Switching between Algorithms based on Trajectory Features
Diederick Vermetten, Hao Wang, Kevin Sim +1
Dynamic algorithm selection aims to exploit the complementarity of multiple optimization algorithms by switching between them during the search. While these kinds of dynamic algori…
Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches
Mohamad Alissa, Kevin Sim, Emma Hart
We propose a novel technique for algorithm-selection, applicable to optimisation domains in which there is implicit sequential information encapsulated in the data, e.g., in online…