Representations and evolutionary operators for the scheduling of pump operations in water distribution networks.

Reducing the energy consumption of water distribution networks has never had more significance. The greatest energy savings can be obtained by carefully scheduling the operations of pumps. Schedules can be defined either implicitly, in terms of other elements of the network such as tank levels, or explicitly by specifying the time during which each pump is on/off. The traditional representation of explicit schedules is a string of binary values with each bit representing pump on/off status during a particular time interval. In this paper, we formally define and analyze two new explicit representations based on time-controlled triggers, where the maximum number of pump switches is established beforehand and the schedule may contain less switches than the maximum. In these representations, a pump schedule is divided into a series of integers with each integer representing the number of hours for which a pump is active/inactive. This reduces the number of potential schedules compared to the binary representation, and allows the algorithm to operate on the feasible region of the search space. We propose evolutionary operators for these two new representations. The new representations and their corresponding operations are compared with the two most-used representations in pump scheduling, namely, binary representation and level-controlled triggers. A detailed statistical analysis of the results indicates which parameters have the greatest effect on the performance of evolutionary algorithms. The empirical results show that an evolutionary algorithm using the proposed representations improves over the results obtained by a recent state-of-the-art Hybrid Genetic Algorithm for pump scheduling using level-controlled triggers.

Evolution feature oriented model driven product line engineering approach for synergistic and dynamic service evolution in Clouds: four kinds of schema.

The proposed research will focus on developing a novel approach to solve Software Service Evolution problems in Computing Clouds. The approach will support dynamic evolution of the software service in clouds via a set of discovered evolution patterns. An initial survey informed us that such an approach does not exist yet and is in urgent need. Evolution Requirement can be classified into evolution features; researchers can describe the whole requirement by using evolution feature typology, the typology will define the relation and dependency between each features. After the evolution feature typology has been constructed, evolution model will be created to make the evolution more specific. Aspect oriented approach can be used for enhance evolution feature-model modularity. Aspect template code generation technique will be used for model transformation in the end. Product Line Engineering contains all the essential components for driving the whole evolution process.

Work on the Move: mobile and nomadic working methodologies.

‘Work on the move’ is a design, process-driven methodology, which uses multiple locations within an outdoors setting and movement between locations, all of which function as learning places, confined to a specified time period. Between 2012 and 2015, a team of international Higher Education product design educators (all members of Carousel, a co-operation of Erasmus members in Zwolle, Edinburgh, Nantes, Rome, Kortrijk and Oslo), industry professionals and product design students developed and tested four case studies. Each case study was conducted in a different international location and was constructed with a different focus, to help define and refine a definitive working methodology. ‘Work on the move’ explores the influence of ‘place’ upon design, in terms of the impact it has on productivity and creative problem-solving, when working away from the traditional studio/office-based environment. It also explores the significance of shared place, when working directly with a client in situ, and experiencing the place-based influences upon their businesses. While identifying location as part of the design process, the study also seeks to understand the effects of time restriction and working in transit upon creativity and productivity, within the context of specific projects.

Model Selection Methodology in Supervised Learning with Evolutionary Computation

Rowland, J.J. (2003) Model Selection Methodology in Supervised Learning with Evolutionary Computation. BioSystems 72, 1-2, pp 187-196, Nov

Generalisation and Model Selection in Supervised Learning with Evolutionary Computation

Rowland, J. J. (2003) Generalisation and Model Selection in Supervised Learning with Evolutionary Computation. European Workshop on Evolutionary Computation in Bioinformatics: EvoBio 2003. Lecture Notes in Computer Science (Springer), Vol 2611, pp 119-130

Interpreting Analytical Spectra with Evolutionary Computation

Rowland, J.J. (2002) Interpreting Analytical Spectra with Evolutionary Computation. In: Fogel, G.B. and Corne, D.W. (eds), Evolutionary Computation in Bioinformatics. Morgan Kaufmann, San Francisco, pp 341--365, ISBN 1-55860-797-8

Data-driven fuzzy rule generation and its application for student academic performance evaluation

K. Rasmani and Q. Shen. Data-driven fuzzy rule generation and its application for student academic performance evaluation. Applied Intelligence, 25(3):305-319, 2006.

A scenario-driven decision support system for serious crime investigation

Q. Shen, J. Keppens, C. Aitken, B. Schafer, and M. Lee. A scenario driven decision support system for serious crime investigation. Law, Probability and Risk, 5(2):87-117, 2006. Sponsorship: UK Engineering and Physical Sciences Research Council grant GR/S63267; partially supported by grant GR/S98603

Probabilistic abductive computation of evidence collection strategies in crime investigation

J. Keppens, Q. Shen and B. Schafer. Probabilistic abductive computation of evidence collection strategies in crime investigation. Proceedings of the 10th International Conference on Artificial Intelligence and Law, pages 215-225.

Fuzzy rules from ant-inspired computation

M. Galea and Q. Shen. Fuzzy rules from ant-inspired computation. Proceedings of the 13th International Conference on Fuzzy Systems, pages 1691-1696, 2004.

Error-driven active learning in growing radial basis function networks for early robot learning

Q. Meng and M.H. Lee, 'Error-driven active learning in growing radial basis function networks for early robot learning', 2006 IEEE International Conference on Robotics and Automation (IEEE ICRA 2006), 2984-90, Orlando, Florida, USA.

Fuzzy-Rough Feature Significance for Fuzzy Decision Trees

R. Jensen and Q. Shen, 'Fuzzy-Rough Feature Significance for Fuzzy Decision Trees,' in Proceedings of the 2005 UK Workshop on Computational Intelligence, pp. 89-96, 2005.

Coronal loops heated by turbulence-driven Alfven waves

Li, Xing, Habbal, S. R., 'Coronal loops heated by turbulence-driven Alfven waves', The Astrophysical Journal, (2003) 598(2) pp.L125-L128 RAE2008