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Unifying Data Driven Decision Making and Evolutionary Computing Through Mathematics

   School of Computing

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  Dr Shufan Yang  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Decisions that were traditionally based primarily on intuition and experience are being challenged by a data-driven decision-making style. Decision making is undergoing a shift at the level of the individual, as it progresses towards the group level. This project will focus on linking up mathematical and computational evolutionary computing algorithms (around the theme of multi-agent networks) for data driven decision making. The potential application can be used in operational research in healthcare which enables policy makers to perceive information intelligently in complex uncontrolled real-world environments. The PhD student will gain substantial knowledge in fields such as evolutionary computing, reinforcement learning, machine learning, deep learning, and data analytics.

Academic qualifications:

A first degree in a relevant scientific discipline, such as computer science, engineering, mathematics, physics, or medicine. Desirable skills include mathematics, statistics, machine learning, computer vision, deep learning, robotics, natural language processing, human-computer interfaces, and software engineering.

English language requirement

IELTS score must be at least 6.5. Other equivalent qualifications will be accepted. Full details of the University’s policy are available online.

Essential attributes:

Edinburgh Napier University is committed to promoting equality and diversity in our staff and student community

Funding Notes

This is an unfunded position.


Emma Hart and Kevin Sim, On Constructing Ensembles for Combinatorial Optimisation Evolutionary Computation 2018 26:1, 67-87 , 2018

Mohamad Alissa, Kevin Sim, and Emma Hart. 2019. Algorithm selection using deep learning without feature extraction. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '19). Association for Computing Machinery, New York, NY, USA, 198–206. DOI:
Yang, S., Wong-Lin, K., Andrew, J., Mak, T. and McGinnity, T. M. (2018) A Neuro-inspired Visual Tracking Method Based on Programmable System-on-chip Platform. Neural Computing and Applications, 30(9), pp. 2697-2708. (doi:10.1007/s00521-017-2847-5)
Yang, S., Wong-Lin, K., Rano, I. and Lindsay, A. (2018) A Single Chip System for Sensor Data Fusion Based on a Drift-diffusion Model. In: Intelligent Systems Conference (IntelliSys) 2018, London, UK, 7-8 Sept 2018, pp. 198-201. ISBN 9781509064359 (doi:10.1109/IntelliSys.2017.8324291)
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