All Outputs (8)

Opportunities and challenges for monitoring terrestrial biodiversity in the robotics age (2025)
Journal Article
Pringle, S., Dallimer, M., Goddard, M. A., Le Goff, L. K., Hart, E., Langdale, S. J., Fisher, J. C., Abad, S.-A., Ancrenaz, M., Angeoletto, F., Auat Cheein, F., Austen, G. E., Bailey, J. J., Baldock, K. C. R., Banin, L. F., Banks-Leite, C., Barau, A. S., Bashyal, R., Bates, A. J., Bicknell, J. E., …Davies, Z. G. (2025). Opportunities and challenges for monitoring terrestrial biodiversity in the robotics age. Nature Ecology & Evolution, 9(6), 1031-1042. https://doi.org/10.1038/s41559-025-02704-9

With biodiversity loss escalating globally, a step change is needed in our capacity to accurately monitor species populations across ecosystems. Robotic and autonomous systems (RAS) offer technological solutions that may substantially advance terrest... Read More about Opportunities and challenges for monitoring terrestrial biodiversity in the robotics age.

XAI for Algorithm Configuration and Selection (2025)
Book Chapter
Thomson, S. L., Hart, E., & Renau, Q. (2025). XAI for Algorithm Configuration and Selection. In N. van Stein, & A. V. Kononova (Eds.), Explainable AI for Evolutionary Computation. Springer. https://doi.org/10.1007/978-981-96-2540-6_6

In this chapter, we consider, formalise, and demonstrate the ways in which XAI can assist or inform algorithm selection and configuration. Reviewing the literature, we notice and taxonomise a much broader and more diverse notion of XAI than is typica... Read More about XAI for Algorithm Configuration and Selection.

Beyond the Hype: Benchmarking LLM-Evolved Heuristics for Bin Packing (2025)
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 methods, particularly in the field of combinatorial optimisation. An escalating... Read More about Beyond the Hype: Benchmarking LLM-Evolved Heuristics for Bin Packing.

Algorithm Selection with Probing Trajectories: Benchmarking the Choice of Classifier Model (2025)
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 encapsulate information about how an algorithm performs on an instance, rather... Read More about Algorithm Selection with Probing Trajectories: Benchmarking the Choice of Classifier Model.

Stalling in Space: Attractor Analysis for any Algorithm (2025)
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 optimisation algorithms. Local optima networks (LONs) have been especially dominant in... Read More about Stalling in Space: Attractor Analysis for any Algorithm.

Into the Black Box: Mining Variable Importance with XAI (2025)
Presentation / Conference Contribution
Hunter, K., Thomson, S. L., & Hart, E. (2025, April). Into the Black Box: Mining Variable Importance with XAI. Presented at Evostar 2025, Trieste, Italy

Recent works have shown that the idea of mining search spaces to train machine learning models can facilitate increasing understanding of variable importance in optimisation problems. However , so far, the problems studied have typically either been... Read More about Into the Black Box: Mining Variable Importance with XAI.

’Bots on the Ground vs Boots on the Ground: The Future of Robots in Terrestrial Ecological Surveying (2025)
Journal Article
White, P., Le Goff, L., Emery, L., Abrahams, C., Findlay, M., Cook, J., Macleod, K., Deacon, L., Reason, P., Stanhope, K., Wale, M., Hart, E., & Diele, K. (2025). ’Bots on the Ground vs Boots on the Ground: The Future of Robots in Terrestrial Ecological Surveying. In Practice (CIEEM), 27, 47-52

At the 2023 CIEEM Modernising Ecology conference, a robot greeted the attendees as they arrived. Was it a glimpse into the future? As with other technologies, robots have the potential to revolutionise ecological surveying, yet this comes with both o... Read More about ’Bots on the Ground vs Boots on the Ground: The Future of Robots in Terrestrial Ecological Surveying.

An Evaluation of Domain-agnostic Representations to Enable Multi-task Learning in Combinatorial Optimisation (2025)
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 developing a more general classifier that does not require specialised info... Read More about An Evaluation of Domain-agnostic Representations to Enable Multi-task Learning in Combinatorial Optimisation.