BayesPiles: Visualisation Support for Bayesian Network Structure Learning
Journal Article
Vogogias, A., Kennedy, J., Archambault, D., Bach, B., Smith, V. A., & Currant, H. (2018)
BayesPiles: Visualisation Support for Bayesian Network Structure Learning. ACM transactions on intelligent systems and technology, 10(1), 1-23. https://doi.org/10.1145/3230623
We address the problem of exploring, combining and comparing large collections of scored, directed networks for understanding inferred Bayesian networks used in biology. In th...
On the comparison of initialisation strategies in differential evolution for large scale optimisation
Journal Article
Segredo, E., Paechter, B., Segura, C., & González-Vila, C. I. (2018)
On the comparison of initialisation strategies in differential evolution for large scale optimisation. Optimization Letters, 12(1), 221-234. https://doi.org/10.1007/s11590-017-1107-z
Differential Evolution (DE) has shown to be a promising global opimisation solver for continuous problems, even for those with a large dimensionality. Different previous works...
Combined vs. Separate Views in Matrix-based Graph Analysis and Comparison.
Conference Proceeding
Melville, A., Graham, M., & Kennedy, J. (2011)
Combined vs. Separate Views in Matrix-based Graph Analysis and Comparison. In 15th International Conference on Information Visualisation (IV), 2011https://doi.org/10.1109/IV.2011.49
While much work has been done in the area of visualization for analysis of graphs, relatively little research exists into how best to use visualization for comparing graphs. W...
FCA interpretation of relation algebra.
Conference Proceeding
Priss, U. (2006)
FCA interpretation of relation algebra. https://doi.org/10.1007/11671404_17
This paper discusses an interpretation of relation algebra and fork algebra with respect to FCA contexts. In this case, "relation algebra" refers to the DeMorgan-Peirce-Schroe...
Parallelization of the nearest-neighbour search and the cross-validation error evaluation for the kernel weighted k-nn algorithm applied to large data dets in matlab
Conference Proceeding
Rubio, G., Guillen, A., Pomares, H., Rojas, I., Paechter, B., Glosekotter, P., & Torres-Ceballos, C. I. (2009)
Parallelization of the nearest-neighbour search and the cross-validation error evaluation for the kernel weighted k-nn algorithm applied to large data dets in matlab. In HPCS '09. International Conference on High Performance Computing & Simulation, 2009https://doi.org/10.1109/hpcsim.2009.5192804
The kernel weighted k-nearest neighbours (KWKNN) algorithm is an efficient kernel regression method that achieves competitive results with lower computational complexity than ...
Machine independent algorithm for concurrent finite-element problems
Conference Proceeding
Buchanan, W., Buchanan, W. J., & Gupta, N. K. (1996)
Machine independent algorithm for concurrent finite-element problems. In 3rd International IEEE Conference on Computation in Electromagnetics, 1996, 17-20. doi:10.1049/cp:19960150
The finite-element method, initially developed for mechanical and civil engineering applications, is now applied to electromagnetics. This paper describes how parallel process...
Using ERMIA for the Evaluation of a Theorem Prover Interface
Conference Proceeding
Jackson, M., Benyon, D., & Lowe, H. (2004)
Using ERMIA for the Evaluation of a Theorem Prover Interface. In R. Backhouse (Ed.), Proceedings of the 4th International Workshop on User Interfaces for Theorem Provers, 104-111
ERMIA (Entity-Relationship Modelling of Information Artefacts) provides an extension to entity-relationship
modelling techniques to provide a structural representation of the ...
Finding feasible timetables using group-based operators.
Journal Article
Lewis, R. M. R. & Paechter, B. (2007)
Finding feasible timetables using group-based operators. IEEE Transactions on Evolutionary Computation. 11, 397-413. doi:10.1109/TEVC.2006.885162. ISSN 1089-778X
This paper describes the applicability of the so-called "grouping genetic algorithm" to a well-known version of the university course timetabling problem. We note that there a...
Hyper-heuristics: learning to combine simple heuristics in bin-packing problems.
Conference Proceeding
Ross, P., Schulenburg, S., Marin-Blazquez, J. G. & Hart, E. (2002)
Hyper-heuristics: learning to combine simple heuristics in bin-packing problems. ISBN 1558608788
Evolutionary algorithms (EAs) often appear to be a ‘black box’, neither offering worst-case bounds nor any guarantee of optimality when used to solve individual problems. They...
Learning a procedure that can solve hard bin-packing problems: a new GA-based approach to hyperheuristics.
Conference Proceeding
Ross, P., Marin-Blazquez, J. G., Schulenburg, S. & Hart, E. (2003)
Learning a procedure that can solve hard bin-packing problems: a new GA-based approach to hyperheuristics
The idea underlying hyper-heuristics is to discover some
combination of familiar, straightforward heuristics that performs very well across a whole range of problems. To be wo...