Meme fitness and memepool sizes in coevolutionary memetic algorithms
(2010)
Presentation / Conference
Smith, J. (2010, July). Meme fitness and memepool sizes in coevolutionary memetic algorithms. Paper presented at Proceedings 2010 World Conference on Computational Intelligence, Barcelona, Spain
Outputs (126)
Analytically redundant controllers for fault tolerance: Implementation with separation of concerns (2010)
Presentation / Conference
Hameed, K., Williams, R., & Smith, J. (2010, June). Analytically redundant controllers for fault tolerance: Implementation with separation of concerns. Paper presented at American Control Conference (ACC), Baltimore, Maryland, USA
Creating diverse nearest-neighbour ensembles using simultaneous metaheuristic feature selection (2010)
Journal Article
Tahir, M. A., & Smith, J. (2010). Creating diverse nearest-neighbour ensembles using simultaneous metaheuristic feature selection. Pattern Recognition Letters, 31(11), 1470-1480. https://doi.org/10.1016/j.patrec.2010.01.030The nearest-neighbour (1NN) classifier has long been used in pattern recognition, exploratory data analysis, and data mining problems. A vital consideration in obtaining good results with this technique is the choice of distance function, and corresp... Read More about Creating diverse nearest-neighbour ensembles using simultaneous metaheuristic feature selection.
User-centric image segmentation using an interactive parameter adaptation tool (2010)
Journal Article
Pauplin, O., Caleb-Solly, P., & Smith, J. (2010). User-centric image segmentation using an interactive parameter adaptation tool. Pattern Recognition, 43(2), 519-529. https://doi.org/10.1016/j.patcog.2009.03.007Creating successful machine vision systems often begins a process of developing customised reliable image segmentation algorithms for the detection, and possibly categorisation of regions of interest within images. This can require significant invest... Read More about User-centric image segmentation using an interactive parameter adaptation tool.
What are evolutionary algorithms? (2010)
Book Chapter
Eiben, A. E., & Smith, J. (2010). What are evolutionary algorithms?. In C. Cotta, & F. Neri (Eds.), Handbook of Memetic Algorithms (9-27). Berlin, Heidelberg, New York: Springer
Evolutionary algorithms (2010)
Book Chapter
Eiben, A. E., & Smith, J. (2010). Evolutionary algorithms. In F. Neri, C. Cotta, & P. Moscato (Eds.), Handbook of Memetic Algorithms (9-27). Springer
Self-adaptive and coevolving MAs (2010)
Book Chapter
Smith, J. (2010). Self-adaptive and coevolving MAs. In C. Cotta, F. Neri, & P. Moscato (Eds.), Handbook of Memetic Algorithms. Berlin, Heidelberg, New York: Springer
Separation of fault tolerance and non-functional concerns: Aspect oriented patterns and evaluation (2010)
Journal Article
Hameed, K., Williams, R., & Smith, J. (2010). Separation of fault tolerance and non-functional concerns: Aspect oriented patterns and evaluation. Journal of Software Engineering and Applications, 3(4), 303-418. https://doi.org/10.4236/jsea.2010.34036
Software fault tolerance: An aspect oriented approach (2010)
Book Chapter
Hameed, K., Williams, R., & Smith, J. (2010). Software fault tolerance: An aspect oriented approach. In L. Gelman (Ed.), Electronic Engineering and Computing Technology (153-164). Springer
Human-machine interaction issues in quality control based on online image classification (2009)
Journal Article
Lughofer, E., Smith, J., Tahir, M. A., Caleb-Solly, P., Eitzinger, C., Sannen, D., & Nuttin, M. (2009). Human-machine interaction issues in quality control based on online image classification. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 39(5), 960-971. https://doi.org/10.1109/TSMCA.2009.2025025This paper considers on a number of issues that arise when a trainable machine vision system learns directly from humans. We contrast this to the "normal" situation where machine learning (ML) techniques are applied to a "cleaned" data set which is c... Read More about Human-machine interaction issues in quality control based on online image classification.