All Outputs (9)

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], 5(2), article number 12. 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..

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..

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..

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..

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..

Towards explainable metaheuristics: PCA for trajectory mining in evolutionary algorithms. (2021)
Presentation / Conference Contribution
FYVIE, M., MCCALL, J.A.W. and CHRISTIE, L.A. 2021. Towards explainable metaheuristics: PCA for trajectory mining in evolutionary algorithms. In Bramer, M. and Ellis, R (eds.) Artificial intelligence XXXVIII: proceedings of 41st British Computer Society's Specialist Group on Artificial Intelligence (SGAI) Artificial intelligence international conference 2021 (AI-2021) (SGAI-AI 2021), 14-16 December 2021, [virtual conference]. Lecture notes in computer science, 13101. Cham: Springer [online], pages 89-102. Available from: https://doi.org/10.1007/978-3-030-91100-3_7

The generation of explanations regarding decisions made by population-based meta-heuristics is often a difficult task due to the nature of the mechanisms employed by these approaches. With the increase in use of these methods for optimisation in indu... Read More about Towards explainable metaheuristics: PCA for trajectory mining in evolutionary algorithms..

Non-deterministic solvers and explainable AI through trajectory mining. (2021)
Presentation / Conference Contribution
FYVIE, M., MCCALL, J.A.W. and CHRISTIE, L.A. 2021. Non-deterministic solvers and explainable AI through trajectory mining. In Martin, K., Wiratunga, N. and Wijekoon, A. (eds.) SICSA XAI workshop 2021: proceedings of 2021 SICSA (Scottish Informatics and Computer Science Alliance) eXplainable artificial intelligence workshop (SICSA XAI 2021), 1 June 2021, [virtual conference]. CEUR workshop proceedings, 2894. Aachen: CEUR-WS [online], session 4, pages 75-78. Available from: http://ceur-ws.org/Vol-2894/poster2.pdf

Traditional methods of creating explanations from complex systems involving the use of AI have resulted in a wide variety of tools available to users to generate explanations regarding algorithm and network designs. This however has traditionally bee... Read More about Non-deterministic solvers and explainable AI through trajectory mining..