All Outputs (3)

A dominance-based surrogate classifier for multi-objective evolutionary algorithms. (2024)
Presentation / Conference Contribution
BANDA, T.M. and ZĂVOIANU, A.-C. 2025. A dominance-based surrogate classifier for multi-objective evolutionary algorithms. In Bramer, M. and Stahl, F. (eds.) Artificial Intelligence XLI: proceedings of the 44th SGAI (Specialist Group on Artificial Intelligence) International conference on artificial intelligence 2024 (AI 2024), 17-19 December 2024, Cambridge, UK. Lecture notes in computer science, 15446. Cham: Springer [online], part I, pages 268-281. Available from: https://doi.org/10.1007/978-3-031-77915-2_19

The application of Multi-Objective Evolutionary Algorithms (MOEAs) is often constrained when addressing computationally expensive Multi-Objective Optimisation Problems (MOOPs). To mitigate this, we propose a dominance-based surrogate classifier that... Read More about A dominance-based surrogate classifier for multi-objective evolutionary algorithms..

Comparison of simulated annealing and evolution strategies for optimising cyclical rosters with uneven demand and flexible trainee placement. (2023)
Presentation / Conference Contribution
COLLINS, J., ZĂVOIANU, A.-C. and MCCALL, J.A.W. 2023. Comparison of simulated annealing and evolution strategies for optimising cyclical rosters with uneven demand and flexible trainee placement. In Bramer, M. and Stahl, F. (eds.) Artificial intelligence XL: proceedings of the 43rd SGAI (Specialist Group on Artificial Intelligence) Artificial intelligence international conference 2023 (AI-2023), 12-14 December 2023, Cambridge, UK. Lecture notes in computer science, 14381. Cham: Springer [online], pages 451-464. Available from: https://doi.org/10.1007/978-3-031-47994-6_39

Rosters are often used for real-world staff scheduling requirements. Multiple design factors such as demand variability, shift type placement, annual leave requirements, staff well-being and the placement of trainees need to be considered when constr... Read More about Comparison of simulated annealing and evolution strategies for optimising cyclical rosters with uneven demand and flexible trainee placement..

Explaining a staff rostering problem by mining trajectory variance structures. (2023)
Presentation / Conference Contribution
FYVIE, M., MCCALL, J.A.W., CHRISTIE, L.A., ZĂVOIANU, A.-C., BROWNLEE, A.E.I. and AINSLIE, R. 2023. Explaining a staff rostering problem by mining trajectory variance structures. In Bramer, M. and Stahl, F. (eds.) Artificial intelligence XL: proceedings of the 43rd SGAI international conference on artificial intelligence (AI-2023), 12-14 December 2023, Cambridge, UK. Lecture notes in computer science, 14381. Cham: Springer [online], pages 275-290. Available from: https://doi.org/10.1007/978-3-031-47994-6_27

The use of Artificial Intelligence-driven solutions in domains involving end-user interaction and cooperation has been continually growing. This has also lead to an increasing need to communicate crucial information to end-users about algorithm behav... Read More about Explaining a staff rostering problem by mining trajectory variance structures..