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. The group has an excellent international reputation: it will host the 14th International Conference on Parallel Problem Solving from Nature in Edinburgh in 2016, with Prof. Emma Hart and Prof. Ben Paechter as General Chairs.  The group inaugurated the international EvoStar Conference in 1998 and has run the conference since then, overseeing its growth into 4 co-located conferences.  Staff regularly chair tracks at GECCO and ALife. Prof. Hart and Prof. Paechter are Associate Editors of the Evolutionary Computation Journal (MIT Press). The group has a sustained record of attracting funding from both international (EU) and national funding bodies, including EPSRC and Leverhulme.  We have an excellent record of encouraging collaborative working with other researchers both in the UK and worldwide, for example hosting visiting international PhD students and visiting researchers, and writing joint publications with external authors.

 The group has a particular interest in application of nature-inspired techniques to practical optimisation where we have a track record of working with industry. Current projects include use of evolution in design, optimisation of cutting patterns in the Forestry Industry, scheduling in the Health Care sector, and vehicle routing, with a specific emphasis on reducing carbon emissions, (working in conjunction with Edinburgh Napier’s Transport Research Institute).  However, we also conduct fundamental research with the goal of improving understanding of bio-inspired optimisation and learning algorithms, for example covering evolutionary robotics, evolution of societies and the hybridisation of machine-learning and optimisation.

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Optimising bin packing

This demo shows a visual representation of a bin packing algorithm. The challenge is to pack the items (shown here as coloured rectangles) into the smallest space.

Try our interactive vehicle routing demo

Businesses increasingly face challenges finding the optimum balance between business drivers including, Cost vs Revenue, customer experience, resource utlisation and environmental impact.
This demonstrator illustrates the way in which Optimisation Algorithms assist with Vehicle Routing.

Contact linked in profile of Bio-inspired Computing web page for Bio-inspired Computing

+44 (0)131 455 2783

Emma Hart
Room C54
Merchiston Campus
10 Colinton Road
EH10 5DT

Selected Publications

Hart, E., Sim, K., Paechter, B. (2015). A Lifelong Learning Hyper-heuristic Method for Bin Packing. Evolutionary Computation, 23, (1), 37-67.

Sim, K., Hart, E. (2014). An Improved Immune Inspired Hyper-Heuristic for Combinatorial Optimisation Problems. In: (Ed.) Proceedings of GECCO 2014 (Genetic and Evolutionary Computation Conference), , () ( ed.). (pp. ). : . ACM Digital Library.

Hart, E., Sim, K. (2014). On the life-long learning capabilities of a NELLI*: a hyper-heuristic optimisation system. In: (Ed.) Proceedings of PPSN, 13th International Conference on Parallel problem Solving from Nature, , () ( ed.). (pp. ). : . Springer.

Capodieci, N., Hart, E., Cabri, G. (2014). Idiotypic networks for evolutionary controllers in virtual creatures. In: (Ed.) Proceedings of ALife, Fourteenth International Conference on the Synthesis and Simulation of Living Systems, , () ( ed.). (pp. ). : . MIT Press.

Hart, E., Sim, K., Urquhart, N. (2014). A Real-World Employee Scheduling and Routing Application. In: (Ed.) GECCO Comp '14 Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion, , () ( ed.). (pp. 1239-1242). : . ACM Digital Library.

Hart, E., Brook, A., Brook-O'Donnell, M., Hone, A., Hughes, T., Smith, R. (2014). General and craniofacial development are complex adaptive processes influenced by diversity. Australian Dental Journal, 59, (1), 1-10.