Algorithm Selection with Probing Trajectories: Benchmarking the Choice of Classifier Model
Presentation / Conference Contribution
Renau, Q., & Hart, E. (2025, April)
Algorithm Selection with Probing Trajectories: Benchmarking the Choice of Classifier Model. Presented at EvoSTAR 2025, Trieste, Italy
Recent approaches to training algorithm selectors in the black-box optimisation domain have advocated for the use of training data that is 'algorithm-centric' in order to enca...
Beyond the Hype: Benchmarking LLM-Evolved Heuristics for Bin Packing
Presentation / Conference Contribution
Sim, K., Hart, E., & Renau, Q. (2025, April)
Beyond the Hype: Benchmarking LLM-Evolved Heuristics for Bin Packing. Presented at EvoSTAR 2025, Trieste, Italy
Coupling Large Language Models (LLMs) with Evolutionary Algorithms has recently shown significant promise as a technique to design new heuristics that outperform existing meth...
Stalling in Space: Attractor Analysis for any Algorithm
Presentation / Conference Contribution
Thomson, S. L., Renau, Q., Vermetten, D., Hart, E., van Stein, N., & Kononova, A. V. (2025, April)
Stalling in Space: Attractor Analysis for any Algorithm. Paper presented at EvoStar 2025, Trieste, Italy
Network-based representations of fitness landscapes have grown in popularity in the past decade; this is probably because of growing interest in explainability for optimisatio...
An Evaluation of Domain-agnostic Representations to Enable Multi-task Learning in Combinatorial Optimisation
Presentation / Conference Contribution
Stone, C., Renau, Q., Miguel, I., & Hart, E. (2024, June)
An Evaluation of Domain-agnostic Representations to Enable Multi-task Learning in Combinatorial Optimisation. Presented at 18th Learning and Intelligent Optimization Conference, Ischia, Italy
We address the question of multi-task algorithm selection in combinatorial optimisation domains. This is motivated by a desire to simplify the algorithm-selection pipeline by ...
Evaluating the Robustness of Deep-Learning Algorithm-Selection Models by Evolving Adversarial Instances
Presentation / Conference Contribution
Hart, E., Renau, Q., Sim, K., & Alissa, M. (2024, September)
Evaluating the Robustness of Deep-Learning Algorithm-Selection Models by Evolving Adversarial Instances. Presented at 18th International Conference on Parallel Problem Solving From Nature PPSN 2024, Hagenburg, Austria
Deep neural networks (DNN) are increasingly being used to perform algorithm-selection in combinatorial optimisation domains, particularly as they accommodate input representat...
Identifying Easy Instances to Improve Efficiency of ML Pipelines for Algorithm-Selection
Presentation / Conference Contribution
Renau, Q., & Hart, E. (2024, September)
Identifying Easy Instances to Improve Efficiency of ML Pipelines for Algorithm-Selection. Presented at 18th International Conference, PPSN 2024, Hagenberg, Austria
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 the Utility of Probing Trajectories for Algorithm-Selection
Conference Proceeding
Renau, Q., & Hart, E. (2024)
On the Utility of Probing Trajectories for Algorithm-Selection. In Applications of Evolutionary Computation. EvoApplications 2024 (98-114). https://doi.org/10.1007/978-3-031-56852-7_7
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 desc...
Towards optimisers that `Keep Learning'
Presentation / Conference Contribution
Hart, E., Miguel, I., Stone, C., & Renau, Q. (2023, July)
Towards optimisers that `Keep Learning'. Presented at Companion Conference on Genetic and Evolutionary Computation, Lisbon, Portugal
We consider optimisation in the context of the need to apply an optimiser to a continual stream of instances from one or more domains, and consider how such a system might 'ke...