All Outputs (6)

Explaining a staff rostering problem using partial solutions. (2024)
Presentation / Conference Contribution
CATALANO, G.A.P.I., BROWNLEE, A.E.I., CAIRNS, D., MCCALL, J.A.W., FYVIE, M. and AINSLIE, R. 2025. Explaining a staff rostering problem using partial solutions. 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, 15447. Cham: Springer [online], part II, pages 179-193. Available from: https://doi.org/10.1007/978-3-031-77918-3_13

There are many critical optimisation tasks that metaheuristic approaches have been shown to be able to solve effectively. Despite promising results, users might not trust these algorithms due to their intrinsic lack of interpretability. This paper de... Read More about Explaining a staff rostering problem using partial solutions..

On the multi-objective optimization of wind farm cable layouts with regard to cost and robustness. (2024)
Presentation / Conference Contribution
CHRISTIE, L.A., SAHIN, A., OGUNSEMI, A., ZĂVOIANU, A.-C. and MCCALL, J.A.W. 2024. On the multi-objective optimization of wind farm cable layouts with regard to cost and robustness. In Affenzeller, M., Winkler, S.M., Kononova, A.V. et al. (eds). Parallel problem solving from nature (PPSN XVIII): proceedings of the 18th Parallel problem solving from nature international conference 2024 (PPSN 2024), 14-18 September 2024, Hagenberg, Austria. Lecture notes in computer science, 15151. Cham: Springer [online], pages 367-382. Available from: https://doi.org/10.1007/978-3-031-70085-9_23

Offshore wind farms (OWFs) have emerged as a vital component in the transition to renewable energy, especially for countries like the United Kingdom with abundant shallow coastal waters suitable for wind energy exploitation. As net-zero emissions tar... Read More about On the multi-objective optimization of wind farm cable layouts with regard to cost and robustness..

Mining potentially explanatory patterns via partial solutions. (2024)
Presentation / Conference Contribution
CATALANO, G.A.P.I., BROWNLEE, A.E.I., CAIRNS, D., MCCALL, J. and AINSLIE, R. 2024. Mining potentially explanatory patterns via partial solutions. In GECCO'24 companion: proceedings of the 2024 Genetic and evolutionary computation conference companion 2024 (GECCO'24 companion), 14-18 July 2024, Melbourne, Australia. New York: ACM [online], pages 567-570. Available from: https://doi.org/10.1145/3638530.3654318

We introduce Partial Solutions to improve the explainability of genetic algorithms for combinatorial optimization. Partial Solutions represent beneficial traits found by analyzing a population, and are presented to the user for explainability, but al... Read More about Mining potentially explanatory patterns via partial solutions..

Cost and performance comparison of holistic solution approaches for complex supply chains on a novel linked problem benchmark. (2024)
Presentation / Conference Contribution
OGUNSEMI, A., MCCALL, J., ZAVOIANU, C. and CHRISTIE, L.A. 2024. Cost and performance comparison of holistic solution approaches for complex supply chains on a novel linked problem benchmark. In Proceedings of the Genetic and evolutionary computation conference 2024 (GECCO'24), 14-18 July 2024, Melbourne, Australia. New York: Association for Computing Machinery (ACM) [online], pages 1327- 1335. Available from: https://doi.org/10.1145/3638529.3654163

Modern supply chains are complex structures of interacting units exchanging goods and services. Business decisions made by individual units in the supply chain have knock-on effects on decisions made by successor units in the chain. Linked Optimisati... Read More about Cost and performance comparison of holistic solution approaches for complex supply chains on a novel linked problem benchmark..

A novel surrogate model for variable-length encoding and its application in optimising deep learning architecture. (2024)
Presentation / Conference Contribution
DANG, T., NGUYEN, T.T., MCCALL, J., HAN, K. and LIEW, A.W.-C. 2024. A novel surrogate model for variable-length encoding and its application in optimising deep learning architecture. In Proceedings of the 2024 IEEE (Institute of Electrical and Electronics Engineers) Congress on evolutionary computation (CEC 2024), 30 June - 05 July 2024, Yokohama, Japan. Available from: https://doi.org/10.1109/CEC60901.2024.10611960

Deep neural networks (DNN) has achieved great successes across multiple domains. In recent years, a number of approaches have emerged on automatically finding the optimal DNN configurations. A technique among these approaches which show great promise... Read More about A novel surrogate model for variable-length encoding and its application in optimising deep learning architecture..

Underwater object detection for smooth and autonomous operations of naval missions: a pilot Dataset. (2024)
Presentation / Conference Contribution
YAN, Y., LI, Y., LIN, H., SARKER, M.M.K., REN, J. and MCCALL, J. 2024. Underwater object detection for smooth and autonomous operations of naval missions: a pilot dataset. In Ren, J., Hussain, A., Liao, I.Y. et al. (eds.) Advances in brain inspired cognitive systems: proceedings of the 13th International conference on Brain-inspired cognitive systems 2023 (BICS 2023), 5-6 August 2023, Kuala Lumpur, Malaysia. Lecture notes in computer sciences, 14374. Cham: Springer [online], pages 113-122. Available from: https://doi.org/10.1007/978-981-97-1417-9_11

Underwater object detection is essential for ensuring autonomous naval operations. However, this task is challenging due to the complexities of underwater environments that often degrade image quality, thereby hampering the performance of detection a... Read More about Underwater object detection for smooth and autonomous operations of naval missions: a pilot Dataset..