evolutionary algorithms ()

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.


IGS Demo Software
Control LED for vertical farming.
This project is being undertake in conjunction with Avant Garde Web Services Ltd. The principle aim is to support Avant Garde in the design, implementation and evaluation of an optimisation algorithm. The algorithm will form the basis of a future product offering from Avant Garde.


Paul Lapok
+44 131 455
Eduardo Segredo
Senior Research Fellow
+44 131 455 2789
Simon T. Powers
+44 131 455 2718
Andreas Steyven
Research Student
+44 131 455


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

Salah, A., 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). : . .

Segredo, E., Paechter, B., Hart, E., Gonzalez-Vila, Carlos I. (2016). Hybrid Parameter Control Approach Applied to a Diversity-based Multi-objective Memetic Algorithm for Frequency Assignment Problems. In: (Ed.) 2016 IEEE Congress on Evolutionary Computation (CEC), , () ( ed.). (pp. 1517 - 1524). : . IEEE.

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.

Benyon, D., Mival, O. (2015). Blended Spaces for Collaboration. Journal of Computer Supported Cooperative Work, , (), .

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

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: . .

Urquhart, N. (2015). Optimising the scheduling and planning of urban milk deliveries. In: Mora, A., Squillero, G. (Eds.) Lecture Notes in Computer Science, 9028, () ( ed.). (pp. 604-615). : . Springer International Publishing.

Capodieci, N., Hart, E., Cabri, G. (2014). Artificial Immune System driven evolution in Swarm Chemistry. In: (Ed.) Proceedings of IEEE SASO 2014, , () ( ed.). (pp. ). : . .

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. (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.

Segredo, E., Seguro, C., Coromoto, L., Hart, E. (2014). A fuzzy logic controller applied to a diversity-based multi-objective evolutionary algorithm for single-objective optimisation. Soft Computing, , (), .

Urquhart, N., Scott, C., Hart, E. (2013). Incorporating Emissions Models within a Multi-Objective Vehicle Routing Problem. In: (Ed.) Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation, , () ( ed.). (pp. ). : . ACM.

Urquhart, N., Scott, C., Hart, E. (2013). Using Graphical Information Systems to improve vehicle routing problem instances. In: (Ed.) GECCO'13 Companion, , () ( ed.). (pp. ). : . .

Hunter, D., Tiddeman, B., Perrett, D. (2012). A GENETIC ALGORITHM FOR FACE FITTING. In: Richard, P., Kraus, M., Laramee, Robert S., Braz, J. (Eds.) In Proceedings of the International Conference on Computer Graphics Theory and Applications, , () ( ed.). (pp. ). Rome, Italy: . Scitepress.

Urquhart, N., Hart, E., Scott, C. (2010). Building low CO2 solutions to the Vehicle Routing Problem with Time Windows using an Evolutionary Algorithm. In: (Ed.) Procedings of CEC 2010, , () ( ed.). (pp. ). Barcelona: . IEEE.

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

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.

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.

Urquhart, N. (2006). Real-time construction of aircraft landing schedules using an evolutionary algorithm. In: Keijzer, M. (Ed.) Genetic and Evolutionary Computation Conference (GECCO), , () ( ed.). (pp. ). Seattle, USA: . ACM.

Lewis, R., Paechter, B. (2004). New Crossover Operators for Timetabling with Evolutionary Algorithms. In: Lotfi, A. (Ed.) 5th International Conference on Recent Advances in Soft Computing (RASC 2004), 5, () (5 ed.). (pp. 189-195). Nottingham, England: . .

Arenas, M.G., Collet, P., Eiben, A.E., Jelasity, M., Merelo, J.J., Paechter, B., Preus, M., Schoenauer, M. (2002). A Framework for Distributed Evolutionary Algorithms. In: (Ed.) Proceedings of the seventh Parallel Problem Solving From Nature (PPSN VII), LNCS 2439, () (LNCS 2439 ed.). (pp. 665-675). Granada: . Springer-Verlag.

Urquhart, N., Ross, P., Paechter, B., Chisholm, K. (2002). Solving A Real World Routing Problem using Multiple Evolutionary Algorithms. In: (Ed.) Lecture Notes in Computer Science, , () ( ed.). (pp. ). : . Springer-Verlag.

Urquhart, N., Paechter, B., Chisholm, K. (2001). Street-based Routing Using an Evolutionary Algorithm. In: Boers, E.J.W. (Ed.) EvoWorkshops 2001, , () ( ed.). (pp. 495-504). : . Springer-Verlag.

Eiben, A.E., Jansen, B., Michalewicz, Z., Paechter, B. (2000). Solving CSPs with evolutionary algorithms using self-adaptive constraint weights. In: (Ed.) Genetic and Evolutionary Computation Conference - GECCO 2000, , () ( ed.). (pp. ). : . .

Paechter, B., Baeck, T., Schoenauer, M., Eiben, A.E., Merelo, J.J. (2000). ., “A Distributed Resource Evolutionary Algorithm Machine. In: (Ed.) Special Session on Evolving Information Ecosystems, , () ( ed.). (pp. ). : . .

Urquhart, N., Chisholm, K., Paechter, B. (2000). Optimising An Evolutionary Algorithm for Scheduling. In: Cagnoni, . (Ed.) EvoWorkshops 2000, 1803, () ( ed.). (pp. 307-318). : . Springer-Verlag.

Paechter, B., Rankin, B., Cumming, A., Fogarty, T. (1998). Timetabling the Classes of an Entire University with an Evolutionary Algorithm. In: Beck, T., Schoenauer, M. (Eds.) Parallel Problem Solving from Nature - PPSN V, , () ( ed.). (pp. ). : . Springer-Verlag.

Paechter, B., Cumming, A., Luchian, H. (1995). The Use of Local Search Suggestion Lists for Improving the Solution of Timetable Problems with Evolutionary Algorithms. In: (Ed.) Proceedings of the AISB Workshop on Evolutionary Computing, ComputerScienceSeries, () ( ed.). (pp. ). : . Springer-Verlag.

Paechter, B. (1994). Optimising a Presentation Timetable Using Evolutionary Algorithms. In: (Ed.) Proceedings of the AISB Workshop on Evolutionary Computing, ComputerScienceSeries, () ( ed.). (pp. ). : . Springer-Verlag.

Paechter, B., Luchian, H., Cumming, A., Petriuc, M. (1994). Two Solutions to the General Timetable Problem Using Evolutionary Algorithms. In: (Ed.) Proceedings of the IEEE World Congress in Computational Intelligence, , () (June ed.). (pp. ). : . .