Evolutionary Computing ()

Areas of Expertise

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.


Real World Optimisation with Life-Long Learning (ROLL)
This project aims to improve the current state of the art in developing optimisation tools which are relevant and acceptable to industry. This will be achieved by addressing industrial current concerns regarding the ability of academic optimisation techniques to deal effectively with highly...
The EvoNet Network of Excellence brings together active European researchers and practitioners in the field of evolutionary computing. Originally set up in 1996 with European Commission funding, the EvoNet network provides coordination, support and a web-based infrastructure for research and...
Speckled Computing
To establish a research infrastructure for realising minute (around 1 cubic millimetre) semiconductor specks which can sense, compute and communicate wirelessly. Specks, scattered or sprayed on the person or surfaces, will collaborate as programmable computational networks called specknets.
SCOPE: Server Configuration Optimisation through Parameter Evolution
A Knowledge Transfer Partnership to use Emergent Computing Techniques for Server Configuration Optimisation
This project seeks to provide the technology and software infrastructure necessary to support the next generation of evolving infohabitants in a way that makes that infrastructure universal, open and scalable. The Distributed Resource Evolutionary Algorithm Machine (DREAM) will use existing...
This project is being undertaken in collaboration with McQueens Dairies. The principle aim is investigate the optimisation of drop-off points within milk rounds. The project will establish a suitable GIS platform and build a demonstrator that will allow a suitable algorithm to be tested.
Tatties is an a system that allows events,for example lectures or tutorials, to be timetabled into timeslots and rooms fully automatically. It uses the new technology of evolutionary algorithms to allow timetables to evolve which not only work, but are "good" from the point of view of all concerned.
New Ties
The project is concerned with emergence and complexity in socially-inspired artificial systems. We will study large systems consisting of an environment and an inhabitant population. The main goal of the project is to realize an evolving artificial society capable of exploring the environment and...
This is an EU-funded research project to develop basic methodologies and principles of systemic intelligence for artefacts such as robots that will be capable of steadily growing their knowledge through continued experience. The project involves four teams: one in the School of Computing at Napier...
Metaheuristics are widely used to solve important practical combinatorial optimization problems. But the how and why they work effectively for specific problems and for others not, remains a mystery. The overall goal of the Metaheuristis Network is to explore these mysteries, to structure algorithm...


Sarah Clayton
Research Student
+44 131 455
Emma Hart
Director of CEC
+44 131 455 2783
Alistair Lawson
Associate Professor (Reader)
+44 131 455 2730
Ben Paechter
Director of Research
+44 131 455 2764
Simon T. Powers
+44 131 455 2718
Catherine Scott
Research Student
+44 131 455
Kevin Sim
+44 131 455 2497
Neil Urquhart
+44 131 455 2655
Andrew Cumming
Senior Lecturer / Principal Consultant
+44 131 455 2753
Paul Lapok
+44 131 455
Naghmeh Moradpoor
Lecturer in Cybersecurity and Networks
+44 131 455 2596
Cedric Perret
Enhanced Associate
+44 131 455
Peter Ross
+44 131 455
Eduardo Segredo
Senior Research Fellow
+44 131 455 2789
Andreas Steyven
Research Student
+44 131 455


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), .

Powers, S.T., Schaik, Carel P. van, Lehmann, L. (2016). How institutions shaped the last major evolutionary transition to large-scale human societies. Philosophical Transactions of the Royal Society B: Biological Sciences, 371, (1687), .

Powers, S., Lehmann, L. (2016). When is bigger better? The effects of group size on the evolution of helping behaviours. Biological Reviews, , (), .

Ryan, Paul A., Powers, S., Watson, Richard A. (2016). Social niche construction and evolutionary transitions in individuality. Biology and Philosophy, 31, (1), 59-79.

Steyven, A., Hart, E., Paechter, B. (2016). Understanding Environmental Influence in an Open-Ended Evolutionary Algorithm. In: Handl, J., Hart, E., Lewis, Peter R., Lopez-Ibanez, M., Ochoa, G., Paechter, B. (Eds.) Parallel Problem Solving from Nature – PPSN XIV, , () ( ed.). (pp. ). : . Springer International Publishing.

Hart, E., Steyven, A., Paechter, B. (2015). Improving Survivability in Environment-driven Distributed
 Evolutionary Algorithms through Explicit Relative Fitness and Fitness Proportionate Communication. In: Silva, S. (Ed.) Proceedings of GECCO '15: 2015 Genetic and Evolutionary Computation Conference, , () ( ed.). (pp. ). Madrid, Spain: . ACM SIGEVO.

Power, Daniel A., Watson, Richard A., Szathmáry, E., Mills, R., Powers, S., Doncaster, C. Patrick, Czapp, B. (2015). What can ecosystems learn? Expanding evolutionary ecology with learning theory. Biology Direct, , (), .

Steyven, A., Hart, E., Paechter, B. (2015). The Cost of Communication: Environmental Pressure and Survivability in mEDEA. In: Silva, S. (Ed.) GECCO'15: 2015 Genetic and Evolutionary Computation Conference Companion, , () ( ed.). (pp. ). Madrid, Spain: . ACM SIGEVO.

Urquhart, N., Hart, E., Judson, A. (2015). Multi-Modal Employee Routing with Time Windows in an Urban Environment. In: (Ed.) Proceedings of the 2015 Genetic and Evolutionary Algorithms Conference, , () ( ed.). (pp. ). Madrid: . .

Watson, Richard A., Mills, R., Buckley, Christopher L., Kouvaris, K., Jackson, A., Powers, S., Cox, C., Tudge, S., Davies, A., Kounios, L., Power, D. (2015). Evolutionary connectionism: algorithmic principles underlying the evolution of biological organisation in evo-devo, evo-eco and evolutionary transitions. Evolutionary Biology, , (), .

Bryson, Joanna J., Mitchell, J., Powers, S., Sylwester, K. (2014). Understanding and Addressing Cultural Variation in Costly Antisocial Punishment. In: Gibson, M., Lawson, D. (Eds.) Advances in the Evolutionary Analysis of Human Behaviour, 1, () ( ed.). (pp. 201-222). : . Springer.

Powers, S.T., Lehmann, L. (2014). An evolutionary model explaining the Neolithic transition from egalitarianism to leadership and despotism. Proceedings of the Royal Society B: Biological Sciences, 281, (1791), .

Powers, S. (2013). The circle of life (reviewing E. Coen, Cells to Civilizations: The principles of change that shape life). Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 44, (3), 447–450.

Powers, S., Lehmann, L. (2013). The co-evolution of social institutions, demography, and large-scale human cooperation. Ecology Letters, 16, (11), 1356-1364.

Powers, S., Taylor, Daniel J., Bryson, Joanna J. (2012). Punishment can promote defection in group-structured populations. Journal of Theoretical Biology, 311, (), 107-116.

Powers, S., Watson, Richard A. (2011). Evolution of Individual Group Size Preference Can Increase Group-Level Selection and Cooperation. In: Kampis, G., Karsai, I., Szathmáry, E. (Eds.) Advances in Artificial Life. Darwin Meets von Neumann. Lecture Notes in Computer Science., 5778/2011, () ( ed.). (pp. 53-60). : . Springer.

Powers, S., Heys, C., Watson, Richard A. (2011). How to measure group selection in real-world populations. In: Lenaerts, T., Giacobini, M., Bersini, H., Bourgine, P., Dorigo, M., Doursat, R. (Eds.) Advances in Artificial Life, ECAL 2011, , () ( ed.). (pp. 672-679). : . MIT Press.

Powers, S., Penn, Alexandra S., Watson, Richard A. (2011). The concurrent evolution of cooperation and the population structures that support it. Evolution, 65, (6), 1527-1543.

Snowdon, James R., Powers, S., Watson, Richard A. (2011). Moderate Contact between Sub-populations Promotes Evolved Assortativity Enabling Group Selection. In: Kampis, G., Karsai, I., Szathmáry, E. (Eds.) Advances in Artificial Life. Darwin Meets von Neumann. Lecture Notes in Computer Science, 5778/2011, () ( ed.). (pp. 45-52). : . Springer.

Watson, Richard A., Palmius, N., Mills, R., Powers, S., Penn, Alexandra S. (2011). Can Selfish Symbioses Effect Higher-Level Selection?. In: Kampis, G., Karsai, I., Szathmáry, E. (Eds.) Advances in Artificial Life. Darwin Meets von Neumann. Lecture Notes in Computer Science, 5778/2011, () ( ed.). (pp. 27-36). : . Springer.

AlJassani, B.A., Urquhart, N., Almaini, A.E.A. (2010). Manipulation and Optimization techniques for Boolean logic. IET Computers and Digital Techniques , 4, (3), 227-239.

Powers, S. (2010). Social niche construction: Evolutionary explanations for cooperative group formation (PhD). University of Southampton (Watson, Richard A.).

Scott, C., Urquhart, N., Hart, E. (2010). Influence of Topology and Payload on CO2 Optimised Vehicle Routing. In: Chio, C. (Ed.) Applications of Evolutionary Computation, , () ( ed.). (pp. ). Istanbul: . Springer Berlin / Heidelberg.

Urquhart, N., Scott, C., Hart, E. (2010). Using an evolutionary algorithm to discover low CO2 tours within a Travelling Salesman Problem. In: al., C. De Chio et (Ed.) Evo Applications 2010, Part II, LNCS 6025, () ( ed.). (pp. 421-430). : . Springer-Verlag.

Urquhart, N. (2010, January). Using Real-World Geospatial Data with Evolutionary Algorithms. Paper presented at Universities' Transport Study Group, Plymouth, UK.

AlJassani, B.A., Urquhart, N., Almaini, A.E.A. (2009). Minimization of Incompletely Specified Mixed Polarity Reed Muller Functions using Genetic Algorithm. In: (Ed.) Procedings of the 3rd IEEE international conference on Signal Circuits & Systems, , () ( ed.). (pp. ). Tunisia: . IEEE Computer Society Press.

Urquhart, N., Vogogias, T., McEwan, T. (2009, May). CO2Y The Intelligent Green Solution: Minimising Carbon Emissions by Maximising Shared Travel Opportunity. Paper presented at Scottish Transport Applications Research Conference, Glasgow.

Craenen, B., Paechter, B. (2008). A Conflict Tabu Search Evolutionary Algorithm for Solving Constraint Satisfaction Problems. In: van Hemert, J., Cotta, C. (Eds.) Evolutionary Computation in Combinatorial Optimization, LNCS 4972, () ( ed.). (pp. ). : . Springer.

Lopez-Ibanez, M., Tumula, P., Paechter, B. (2008). Ant Colony Optimization for Optimal Control of Pumps in Water Distribution Networks. Journal of Water Resource Planning and Management, 134, (4), 337-346.

Powers, S., He, J. (2008). A hybrid artificial immune system and Self Organising Map for network intrusion detection. Information Sciences, 178, (15), 3024-3042.

Powers, S., Penn, Alexandra S., Watson, Richard A. (2008). The efficacy of group selection is increased by coexistence dynamics within groups. In: Bullock, S., Noble, J., Watson, Richard A., Bedau, M.A. (Eds.) Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems, , () ( ed.). (pp. 498-505). : . MIT Press.

Lewis, R., Paechter, B., McCollum, B. (2007). Post Enrolment based Course Timetabling: A Description of the Problem Model used for Track Two of the Second International Timetabling Competition. , (), ..

Urquhart, N. (2007). Carbon Friendly Travel Plan Construction Using an Evolutionary Algorithm. In: Thierens, D., Beyer, H., Bongard, J., Branke, J., Clark, J., Cliff, D., Congdon, C., Deb, K., Doerr, B., Kovacs, T., Kumar, S., Miller, Julian F., Moore, J., Neumann, F., Pelikan, M., Riccardo, P. (Eds.) GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, , () ( ed.). (pp. 2269--2269). London, UK: . ACM.

Hart, E., Ross, P., Corne, D. (2005). Evolutionary Scheduling: A Review. Genetic Programming and Evolable Machines, , (2), 191-220.

Gottlieb, J., Hart, E., Middendorf, M., Raidl, G., Reeves, C. (Eds.) (2004). Special Issue: Journal of Mathematical Modelling and Algorithms, 3, (4) ( ed.). : . .