|Position:||Professorial Lead for Predictive Data Analytics|
|Telephone:||+44 (0) 1224 262780|
Duties and Responsibilities
Director of IDEAS Research Institute.
PhD in Mathematics (Algebraic Topology), University of Aberdeen, 1990.
John McCall is a Professor of Computing Science at Robert Gordon University. He works in the Computational Intelligence research group, which he founded in 2003. He has over twenty years research experience in naturally-inspired computing. His research focuses on the study and analysis of a range of naturally-inspired optimization algorithms (genetic algorithms, particle swarm optimisation, ant colony optimisation, estimation of distribution algorithms etc.) and their application to difficult learning and optimisation problems, particularly real-world problems arising in complex engineering and medical / biological systems. Application areas of this research include medical decision support, data modeling of drilling operations, analysis of biological sequences, staff rostering and scheduling, industrial process optimization and bio-control. He has over 90 publications in books, journals and conferences. He has successfully supervised 13 PhD students and has examined over 15 PhD theses.
- Member of the IEEE Intelligent Systems Applications Technical Committee where he chairs the Task Force in Biomedical Engineering Applications.
- Member of IEEE Evolutionary Computing Task Force where he chairs the Task Force in Probabilistic Modelling.
- Complex Systems Engineering research theme leader for the Scottish Informatics and Computing Science Alliance (SICSA).
- Deputy Theme Leader in Complex Systems for the Northern research Partnership.
- Brownlee, A.E.I., McCall, J.A.W., Zhang, Q (2013). Fitness Modelling with Markov Networks, IEEE Transactions on Evolutionary Computation. doi:10.1109/TEVC.2013.2281538
- Regnier-Coudert, O., McCall, J., & Ayodele, M. (2013, July). Geometric-based sampling for permutation optimization. In Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference (GECCO 2013) (pp. 399-406). ACM.
- Yanghui Wu, John McCall, David Corne and Olivier Regnier-Coudert, Landscape Analysis for Hyperheuristic Bayesian Network Structure Learning on Unseen Problems, IEEE Congress on Evolutionary Computation, IEEE 2012, pp 3229 – 3236.
- Alexander E.I. Brownlee, Olivier Regnier-Coudert, John A.W. McCall, Stewart Massie & Stefan Stulajter (2012): An application of a GA with Markov network surrogate to feature selection, International Journal of Systems Science, doi:10.1080/00207721.2012.684449
- Olivier Regnier-Coudert, John McCall, Robert Lothian, Thomas Lam, Sam McClinton, James N’Dow, Machine learning for improved pathological staging of prostate cancer: a performance comparison on a range of classifiers, Artificial Intelligence in Medicine, 2011 http://dx.doi.org/10.1016/j.artmed.2011.11.003
- Alexander E. I. Brownlee, John A. W. McCall, Qingfu Zhang and Deryck F. Brown, Approaches to Selection and their Effect on Fitness Modelling in an Estimation of Distribution Algorithm, in Proceedings 2008 IEEE Congress in Evolutionary Computation (CEC 2008), Hong Kong pp2626-2633.