On the challenges of jointly optimising robot morphology and control using a hierarchical optimisation scheme
Conference Proceeding
Goff, L. K. L., & Hart, E. (2021)
On the challenges of jointly optimising robot morphology and control using a hierarchical optimisation scheme. In GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion (1498-1502). https://doi.org/10.1145/3449726.3463156
We investigate a hierarchical scheme for the joint optimisation of robot bodies and controllers in a complex morphological space. An evolutionary algorithm optimises body-plan...
Nature Inspired Optimisation for Delivery Problems: From Theory to the Real World
Book
Urquhart, N. (2022)
Nature Inspired Optimisation for Delivery Problems: From Theory to the Real World. Cham: Springer. https://doi.org/10.1007/978-3-030-98108-2
This book explains classic routing and transportation problems and solutions, before offering insights based on successful real-world solutions. The chapters in Part I introdu...
Lifelong Learning Machines: Towards Developing Optimisation Systems That Continually Learn
Book Chapter
Hart, E. (2022)
Lifelong Learning Machines: Towards Developing Optimisation Systems That Continually Learn. In A. E. Smith (Ed.), Women in Computational Intelligence: Key Advances and Perspectives on Emerging Topics (187-203). Cham: Springer. https://doi.org/10.1007/978-3-030-79092-9_9
Standard approaches to developing optimisation algorithms tend to involve selecting an algorithm and tuning it to work well on a large set of problem instances from the domain...
Machine Learning Enabled Quantitative Ultrasound Techniques for Tissue Differentiation
Journal Article
Thomson, H., Yang, S., & Cochran, S. (2022)
Machine Learning Enabled Quantitative Ultrasound Techniques for Tissue Differentiation. Journal of Medical Ultrasonics, 49, 517-528. https://doi.org/10.1007/s10396-022-01230-6
Quantitative ultrasound (QUS) infers properties about tissue microstructure from backscattered via radio-frequency ultrasound data. This paper describes how to implement the m...
Building low CO2 solutions to the vehicle routing problem with time windows using an evolutionary algorithm.
Conference Proceeding
Urquhart, N. B., Hart, E., & Scott, C. (2010)
Building low CO2 solutions to the vehicle routing problem with time windows using an evolutionary algorithm. In IEEE Congress on Evolutionary Computation. https://doi.org/10.1109/CEC.2010.5586088
An evolutionary Multi-Objective Algorithm (MOA) is used to investigate the trade-off between CO2 savings, distance and number of vehicles used in a typical vehicle routing pro...
Influence of topology and payload on CO2 optimised vehicle routing
Conference Proceeding
Scott, C., Urquhart, N. B., & Hart, E. (2010)
Influence of topology and payload on CO2 optimised vehicle routing. In Applications of Evolutionary Computing (141-150). https://doi.org/10.1007/978-3-642-12242-2_15
This paper investigates the influence of gradient and payload correction factors used within a CO2 emission model on the solutions to shortest path and travelling salesman pro...
Augmenting Novelty Search with a Surrogate Model to Engineer Meta-Diversity in Ensembles of Classifiers
Conference Proceeding
Cardoso, R. P., Hart, E., Burth Kurka, D., & Pitt, J. (2022)
Augmenting Novelty Search with a Surrogate Model to Engineer Meta-Diversity in Ensembles of Classifiers. In Applications of Evolutionary Computation: EvoApplications 2022 (418-434). https://doi.org/10.1007/978-3-031-02462-7_27
Using Neuroevolution combined with Novelty Search to promote behavioural diversity is capable of constructing high-performing ensembles for classification. However, using grad...
Using graphical information systems to improve vehicle routing problem instances.
Conference Proceeding
Urquhart, N. B., Scott, C., & Hart, E. (2013)
Using graphical information systems to improve vehicle routing problem instances. In C. Blum (Ed.), GECCO'13 Companion (1097-1102). https://doi.org/10.1145/2464576.2466802
This paper makes the assertion that vehicle routing rearch has produced increasingly more powerful problem solvers, but has not increased the realism or compexity of typical p...
A Novel Nomad Migration-Inspired Algorithm for Global Optimization
Journal Article
Lin, N., Fu, L., Zhao, L., Hawbani, A., Tan, Z., Al-Dubai, A., & Min, G. (2022)
A Novel Nomad Migration-Inspired Algorithm for Global Optimization. Computers and Electrical Engineering, 100, Article 107862. https://doi.org/10.1016/j.compeleceng.2022.107862
Nature-inspired computing (NIC) has been widely studied for many optimization scenarios. However, miscellaneous solution space of real-world problem causes it is challenging t...
Deep Learning in Mobile Computing: Architecture, Applications, and Future Challenges
Journal Article
Yang, X., Tan, Z., & Luo, Z. (2021)
Deep Learning in Mobile Computing: Architecture, Applications, and Future Challenges. Mobile Information Systems, 2021, 1-3. https://doi.org/10.1155/2021/9874724
No abstract available.