All Outputs (2)

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..

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. [2023]. Towards explainable metaheuristics: feature extraction from trajectory mining. Expert systems [online], Early View. 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..