Falling within the Information Society Theme
, we are specifically hoping to attract applicants interested in bio-inspired computing and optimisation.
Please contact me for further details.
My interests lie the area of Biologically Inspired Computing, in particular Artificial Immune Systems (AIS). I undertake research in three main areas: optimisation, self-organising and self-adaptive systems, and understanding biological systems.
- Hyper-heuristics as a practical method of solving optimisation problems encountered in the real world, e.g packing, scheduling and routing
- Use of optimisation techniques to minimise carbon emissions and in low-carbon technologies and renewable energy sector
- Optimisation systems that learn from experience and self-improve over time
Understanding biological systems
- How can ideas from complex biological systems effectively be transferred to algorithms for use in engineered systems, through a process of modelling and abstraction ?
- Understanding the role of complex networks in biological systems - in particular, understanding through modelling and simulation how the topology of a biological network ultimately influences the
functionality of that network.
- Fundamentals of Collective, Adaptive Systems
Self-adaptive and Self-organising Systems
- Applying immunological and other biological inspiration to building self-maintaining,
adaptive, autonomous, distributed systems which have
to continuously operate inside some kind of viability zone
- Learning in autonomous systems e.g evolutionary robotics
- Adaptation and learning in distributed systems such as wireless sensor networks
Prof. Hart gained a 1st Class Honours Degree in Chemistry from the University of Oxford, followed by an MSc in Artificial Intelligence from the University of Edinburgh. Her PhD, also from the University of Edinburgh, explored the use of immunology as an inspiration for computing, examining a range of techniques applied to optimisation and data classification problems.
She moved to Edinburgh Napier University in 2000 as a lecturer, and was promoted to a Chair in 2008 in Natural Computation. She continues to research in the area of developing novel bio-inspired techniques for solving a range of real-world optimisation and classification problems, as well as exploring the fundamental properties of immune-inspired computing through modelling and simulation.
Hart, E. (2017). Impact of selection methods on the diversity of many-objective Pareto set approximations. Procedia Computer Science 00 (2017) 000–000 21st International Conference on Knowledge Based and Intelligent Information and Engineering Systems, , (In press), 1-10.
Cabri, G. (2016). Artificial Immunology for Collective Adaptive Systems Design and Implementation. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 11, (2), .
Sim, K. (2016). A Hyper-Heuristic Ensemble Method for Static Job-shop Scheduling. Evolutionary Computation, (pre-print, accepted for publication May 2016), (), .
Paechter, B. (2016). Analysing the performance of migrating birds optimisation approaches for large scale continuous problems. In: (Ed.) Parallel Problem Solving from Nature -- PPSN XIV: 14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings, , () ( ed.). (pp. 134-144). : . Springer International Publishing.
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Blades, A. (2017). Machine Learning for Algorithm Selection (BEng (Hons) Software Engineering Dissertation). Edinburgh Napier University (Hart, E.,
Kahembwe, E. (2014). A Flexible Framework for Analysing Genetic Algorithms In Go (BSc (Hons) Games Development Dissertation). Edinburgh Napier University (Kerridge, J.,
Maroulis, G. (2014). Comparison between Maximum Entropy and Naïve Bayes classifiers: Case study; Appliance of Machine Learning Algorithms to an Odesk’s Corporation Dataset (MSc Information Systems Development Dissertation). Edinburgh Napier University (Hart, E.,
McMillan, C. (2014). Comparison of Pathfinding Algorithms Using the GPGPU (BEng (Hons) Games Development Dissertation). Edinburgh Napier University (Hart, E.,
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