Dr Lee Christie's Outputs (3)

Optimising public transport through the integration of micro and macro-level simulations. (2025)
Presentation / Conference Contribution

The European Commission and the UK aim for net zero emissions in transportation by 2050. This work explores the potential of connected and autonomous vehicles (CAVs) to support this goal by enhancing public transport (PT) via strategic deployment wit... Read More about Optimising public transport through the integration of micro and macro-level simulations..

Optimising public transport through the integration of micro and macro-level simulations. (2025)
Presentation / Conference Contribution
HAN, K., CHRISTIE, L.A., ZĂVOIANU, A.-C. and MCCALL, J.A.W. 2025. Optimising public transport through the integration of micro and macro-level simulations. In Quesada-Arencibia, A., Affenzeller, M. and Moreno-Díaz, R. (eds.) Computer aided systems theory - EUROCAST 2024: revised selected papers of the 19th International conference on Computer-aided systems theory (EUROCAST 2024), 25 February - 1 March 2024, Las Palmas de Gran Canaria, Spain. Lecture notes in computer science, 15172. Cham: Springer [online], part 1, pages 107-121. Available from: https://doi.org/10.1007/978-3-031-82949-9_10

The European Commission and the UK aim for net zero emissions in transportation by 2050. This work explores the potential of connected and autonomous vehicles (CAVs) to support this goal by enhancing public transport (PT) via strategic deployment wit... Read More about Optimising public transport through the integration of micro and macro-level simulations..

Towards explainable metaheuristics: feature mining of search trajectories through principal component projection. (2025)
Journal Article
FYVIE, M., MCCALL, J.A.W. and CHRISTIE, L.A. 2005. Towards explainable metaheuristics: feature mining of search trajectories through principal component projection. ACM transactions on evolutionary learning and optimization [online], Just Accepted. Available from: https://doi.org/10.1145/3731456

While population-based metaheuristics have proven useful for refining and improving explainable AI systems, they are seldom the focus of explanatory approaches themselves. This stems from their inherently stochastic, population-driven searches, which... Read More about Towards explainable metaheuristics: feature mining of search trajectories through principal component projection..