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.
The Bio-Inspired Algorithms group within the Centre for Algorithms, Visualisation and Evolving Systems is a large and thriving group with interests in nature-inspired computing that include Evolutionary Computing, Hyper-Heuristics, Artificial Immune Systems and Swarm Intelligence.
+44 (0)131 455 2783
10 Colinton Road
PhD Project Involvement
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Enhancing the capacity for workplace learning and innovation in Scotland.
Software healing inspired by the immune system.
Exploiting cooperative behaviour to guide open-ended evolution in multi-robot applications. This research is concerned with the evolution and adaptation of robots in evolutionary robotics (ER) in an open-ended evolutionary (OEE) environment. OEE processes are objective free evolutionary...
Visualisation of arguments.
Understanding and designing interactive collaborative spaces. I am currently working on a PhD programme in the Centre for Interaction Design, supervised by Prof. David Benyon and Dr. Oli Mival. I am investigating the state of the art in interactive and...
A model-driven architectural approach for engineering green pervasive systems. With the growing need of the industry and due to high business demands, there have consequently been high demands of IT products to address these needs (majorly seen in the automation of...
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.
Hart, E. (2016). Validating the Grid Diversity Operator: an Infusion Technique for Diversity Maintenance in Population-based Optimisation Algorithms. In: (Ed.) Applications of Evolutionary Computation, 9598, () ( ed.). (pp. 11-26). : . .
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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.,
Sim, K. (2010). Development of a Problem Generator for Bin Packing Problems: An Analysis of Benchmark Problems and Current Stochastic Deterministic Problem Solving Techniques (MSc Advanced Software Engineering Dissertation). Edinburgh Napier University.
See all supervised dissertations