Professor Emma Hart

Director of CEC

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Latest News!   5 year Principal's Research Fellowship, deadline for applications 1st July.

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.

Research Interests

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.

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Areas of Expertise link icon

  • Bio-inspired Computing
    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.
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+44 (0)131 455 2783

Room C54
Merchiston Campus
10 Colinton Road
EH10 5DT

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    PhD Project Involvement

    Craig Thomson (PhD 2016-)
    Exploiting the mobility patterns of mobile sink nodes to improve the efficiency of duty-cycling and routing in wireless sensor networks.
    Dana Khartabil (PhD 2015-)
    Visualisation of arguments.
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    Aurelien Ammeloot (PhD 2013-)
    Understanding and designing interactive collaborative spaces. The combination of personal and Bring Your Own Device (BYOD) technologies with augmented "smart" spaces designed for collaborative work offers new design challenges for the HCI community.
    Lyndsey Jenkins (PhD 2015-)
    Enhancing the capacity for workplace learning and innovation in Scotland.
    Christopher Stone (PhD 2015-)
    A graph grammar approach to generation and modification of heurisitcs for combinatorial optimisation.
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    Andreas Steyven (PhD 2013-)
    Exploiting cooperative behaviour to guide open-ended evolution in multi-robot applications. Swarm robotics is a special case within the general field of robotics. The distributed nature makes it more resilient with no single point of failure. Considering the application in remote locations,...
    See all PhD projects

    Recent Publications

    Martí, L., Segredo, E., Sanchez-Pi, N., 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.

    Capodieci, N., Hart, E., Cabri, G. (2016). Artificial Immunology for Collective Adaptive Systems Design and Implementation. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 11, (2), .

    Hart, E., Sim, K. (2016). A Hyper-Heuristic Ensemble Method for Static Job-shop Scheduling. Evolutionary Computation, (pre-print, accepted for publication May 2016), (), .

    Lalla-Ruiz, E., Segredo, E., Voß, S., Hart, E., 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.

    See all publications

    Supervised dissertations

    Blades, A. (2017). Machine Learning for Algorithm Selection (BEng (Hons) Software Engineering Dissertation). Edinburgh Napier University (Hart, E., Sim, K.).

    Kahembwe, E. (2014). A Flexible Framework for Analysing Genetic Algorithms In Go (BSc (Hons) Games Development Dissertation). Edinburgh Napier University (Kerridge, J., Hart, E.).

    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., Urquhart, N.).

    McMillan, C. (2014). Comparison of Pathfinding Algorithms Using the GPGPU (BEng (Hons) Games Development Dissertation). Edinburgh Napier University (Hart, E., Urquhart, N.).

    See all supervised dissertations