All Outputs (108)

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..

Evolutionary computation and explainable AI: a roadmap to understandable intelligent systems. (2024)
Journal Article
ZHOU, R., BACARDIT, J., BROWNLEE, A.E.I., CAGNONI, S., FYVIE, M., IACCA, G., MCCALL, J., VAN STEIN, N., WALKER, D.J. and HU, T. [2024]. Evolutionary computation and explainable AI: a roadmap to understandable intelligent systems. IEEE Transactions on evolutionary computation [online], Early Access. Available from: https://doi.org/10.1109/TEVC.2024.3476443

Artificial intelligence methods are being increasingly applied across various domains, but their often opaque nature has raised concerns about accountability and trust. In response, the field of explainable AI (XAI) has emerged to address the need fo... Read More about Evolutionary computation and explainable AI: a roadmap to understandable intelligent systems..

Which classifiers are connected to others? An optimal connection framework for multi-layer ensemble systems. (2024)
Journal Article
DANG, T., NGUYEN, T.T., LIEW, A.W.-C., ELYAN, E. and MCCALL, J. 2024. Which classifiers are connected to others? An optimal connection framework for multi-layer ensemble systems. Knowledge-based systems [online], 304, article number 112522. Available from: https://doi.org/10.1016/j.knosys.2024.112522

Ensemble learning is a powerful machine learning strategy that combines multiple models e.g. classifiers to improve predictions beyond what any single model can achieve. Until recently, traditional ensemble methods typically use only one layer of mod... Read More about Which classifiers are connected to others? An optimal connection framework for multi-layer ensemble systems..

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..

Explainability of non-deterministic solvers: explanatory feature generation from the data mining of the search trajectories of population-based metaheuristics. (2024)
Thesis
FYVIE, M. 2024. Explainability of non-deterministic solvers: explanatory feature generation from the data mining of the search trajectories of population-based metaheuristics. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-2565263

Evolutionary algorithms (EAs) are the principal focus of research study in Evolutionary Computing (EC). In EC, naturally occurring processes designed to drive success in nature are simulated for a similar purpose in numerical optimisation. Such proce... Read More about Explainability of non-deterministic solvers: explanatory feature generation from the data mining of the search trajectories of population-based metaheuristics..

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..

Exploring representations for optimising connected autonomous vehicle routes in multi-modal transport networks using evolutionary algorithms. (2024)
Journal Article
HAN, K., CHRISTIE, L.A., ZAVOIANU, A.-C. and MCCALL, J.A.W. 2024. Exploring representations for optimising connected autonomous vehicle routes in multi-modal transport networks using evolutionary algorithms. IEEE transactions on intelligent transportation systems, [online], 25(9), pages 10790-10801. Available from: https://doi.org/10.1109/TITS.2024.3374550

The past five years have seen rapid development of plans and test pilots aimed at introducing connected and autonomous vehicles (CAVs) in public transport systems around the world. While self-driving technology is still being perfected, public transp... Read More about Exploring representations for optimising connected autonomous vehicle routes in multi-modal transport networks using evolutionary algorithms..

Special issue on explainable AI in evolutionary computation. (2024)
Journal Article
BACARDIT, J., BROWNLEE, A., CAGNONI, S., IACCA, G., MCCALL, J. and WALKER, D. (eds.) 2024. Special issue on explainable AI in evolutionary computation. ACM transactions on evolutionary learning and optimization [online], 4(1). Available from: https://dl.acm.org/toc/telo/2024/4/1

Explainable Artificial Intelligence (XAI) has recently emerged as one of the most active areas of research in AI. While Evolutionary Computation (EC) is also a very active research area, the intersection between XAI and EC is still rather unexplored.... Read More about Special issue on explainable AI in evolutionary computation..

Two-layer ensemble of deep learning models for medical image segmentation. (2024)
Journal Article
DANG, T., NGUYEN, T.T., MCCALL, J., ELYAN, E. and MORENO-GARCÍA, C.F. 2024. Two-layer ensemble of deep learning models for medical image segmentation. Cognitive computation [online], 16(3), pages 1141-1160. Available from: https://doi.org/10.1007/s12559-024-10257-5

One of the most important areas in medical image analysis is segmentation, in which raw image data is partitioned into structured and meaningful regions to gain further insights. By using Deep Neural Networks (DNN), AI-based automated segmentation al... Read More about Two-layer ensemble of deep learning models for medical image segmentation..

A weighted ensemble of regression methods for gross error identification problem. (2023)
Presentation / Conference Contribution
DOBOS, D., DANG, T., NGUYEN, T.T., MCCALL, J., WILSON, A., CORBETT, H. and STOCKTON, P. 2023. A weighted ensemble of regression methods for gross error identification problem. In Proceedings of the 2023 IEEE (Institute of Electrical and Electronics Engineers) Symposium series on computational intelligence (SSCI 2023), 5-8 December 2023, Mexico City, Mexico. Piscataway: IEEE [online], pages 413-420. Available from: https://doi.org/10.1109/SSCI52147.2023.10371882

In this study, we proposed a new ensemble method to predict the magnitude of gross errors (GEs) on measurement data obtained from the hydrocarbon and stream processing industries. Our proposed model consists of an ensemble of regressors (EoR) obtaine... Read More about A weighted ensemble of regression methods for gross error identification problem..

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..

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..

Towards explainable metaheuristics: feature extraction from trajectory mining. (2023)
Journal Article
FYVIE, M., MCCALL, J.A.W., CHRISTIE, L.A., BROWNLEE, A.E.I. and SINGH, M. 2025. Towards explainable metaheuristics: feature extraction from trajectory mining. Expert systems [online], 42(1), article number e13494. Available from: https://doi.org/10.1111/exsy.13494

Explaining the decisions made by population-based metaheuristics can often be considered difficult due to the stochastic nature of the mechanisms employed by these optimisation methods. As industries continue to adopt these methods in areas that incr... Read More about Towards explainable metaheuristics: feature extraction from trajectory mining..

Explaining a staff rostering genetic algorithm using sensitivity analysis and trajectory analysis. (2023)
Presentation / Conference Contribution
FYVIE, M., MCCALL, J.A.W., CHRISTIE, L.A. and BROWNLEE, A.E.I. 2023. Explaining a staff rostering genetic algorithm using sensitivity analysis and trajectory analysis. In GECCO’23 companion: proceedings of the 2023 Genetic and evolutionary computation conference companion, 15-19 July 2023, Lisbon, Portugal. New York: ACM [online], pages 1648-1656. Available from: https://doi.org/10.1145/3583133.3596353

In the field of Explainable AI, population-based search metaheuristics are of growing interest as they become more widely used in critical applications. The ability to relate key information regarding algorithm behaviour and drivers of solution quali... Read More about Explaining a staff rostering genetic algorithm using sensitivity analysis and trajectory analysis..

DEFEG: deep ensemble with weighted feature generation. (2023)
Journal Article
LUONG, A.V., NGUYEN, T.T., HAN, K., VU, T.H., MCCALL, J. and LIEW, A.W.-C. 2023. DEFEG: deep ensemble with weighted feature generation. Knowledge-based systems [online], 275, article 110691. Available from: https://doi.org/10.1016/j.knosys.2023.110691

With the significant breakthrough of Deep Neural Networks in recent years, multi-layer architecture has influenced other sub-fields of machine learning including ensemble learning. In 2017, Zhou and Feng introduced a deep random forest called gcFores... Read More about DEFEG: deep ensemble with weighted feature generation..

A comparative study of anomaly detection methods for gross error detection problems. (2023)
Journal Article
DOBOS, D., NGUYEN, T.T., DANG, T., WILSON, A., CORBETT, H., MCCALL, J. and STOCKTON, P. 2023. A comparative study of anomaly detection methods for gross error detection problems. Computers and chemical engineering [online], 175, article 108263. Available from: https://doi.org/10.1016/j.compchemeng.2023.108263

The chemical industry requires highly accurate and reliable measurements to ensure smooth operation and effective monitoring of processing facilities. However, measured data inevitably contains errors from various sources. Traditionally in flow syste... Read More about A comparative study of anomaly detection methods for gross error detection problems..

On the elusivity of dynamic optimisation problems. (2023)
Journal Article
ALZA, J., BARTLETT, M., CEBERIO, J. and MCCALL, J. 2023. On the elusivity of dynamic optimisation problems. Swarm and evolutionary computation [online], 78, article 101289. Available from: https://doi.org/10.1016/j.swevo.2023.101289

The field of dynamic optimisation continuously designs and compares algorithms with adaptation abilities that deal with changing problems during their search process. However, restarting the search algorithm after a detected change is sometimes a bet... Read More about On the elusivity of dynamic optimisation problems..