40 citations · 52 across the 14 of their papers we have counts for
5 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…
Identifying Easy Instances to Improve Efficiency of ML Pipelines for Algorithm-Selection
Quentin Renau, Emma Hart
Algorithm-selection (AS) methods are essential in order to obtain the best performance from a portfolio of solvers over large sets of instances. However, many AS methods rely on an…
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…
Improving Algorithm-Selection and Performance-Prediction via Learning Discriminating Training Samples
Quentin Renau, Emma Hart
The choice of input-data used to train algorithm-selection models is recognised as being a critical part of the model success. Recently, feature-free methods for algorithm-selectio…
On the Utility of Probing Trajectories for Algorithm-Selection
Quentin Renau, Emma Hart
Machine-learning approaches to algorithm-selection typically take data describing an instance as input. Input data can take the form of features derived from the instance descripti…