A comparison of dominance mechanisms and simple mutation on non-stationary problems.
Lewis, J., Hart, E., & Ritchie, G. (1998)
A comparison of dominance mechanisms and simple mutation on non-stationary problems. In Parallel Problem Solving from Nature-PPSN V. , (139-148). https://doi.org/10.1007/BFb0056857
It is sometimes claimed that genetic algorithms using diploid representations will be more suitable for problems in which the environment changes from time to time, as the add...
Towards self-aware PerAda systems.
Hart, E., & Paechter, B. (2010)
Towards self-aware PerAda systems. In E. Hart, C. McEwan, J. Timmis, & A. Hone (Eds.), Artificial Immune Systems: 9th International Conference, ICARIS 2010 Proceedings, 314-216. https://doi.org/10.1007/978-3-642-14547-6_28
Pervasive Adaptation (PerAda) refers to massive-scale pervasive information and communication systems which are capable of autonomously adapting to highly dynamic and open tec...
On the role of the AIS practitioner.
Hart, E., Read, M., McEwan, C., Aickelin, U., & Greensmith, J. (2013)
On the role of the AIS practitioner. In P. Liò, O. Miglino, G. Nicosia, S. Nolfi, & M. Pavone (Eds.), Advances in Artificial Life, ECAL 2013, 891-892. https://doi.org/10.7551/978-0-262-31709-2-ch132
Cognisant of the gulf between engineers and immunologists
that currenty hinders a truly inter-disciplinary approach to
the field of Artificial Immune Systems (AIS), we propose...
Optimising the scheduling and planning of urban milk deliveries.
Urquhart, N. B. (2015)
Optimising the scheduling and planning of urban milk deliveries. In A. M. Mora, & G. Squillero (Eds.), Applications of Evolutionary Computation. , (604-615). https://doi.org/10.1007/978-3-319-16549-3_49
This paper investigates the optimisation of the delivery of dairy products to households in three urban areas. The requirement for the optimisation to be part of the existing ...
On artificial immune systems and swarm intelligence
Timmis, J., Andrews, P., & Hart, E. (2010)
On artificial immune systems and swarm intelligence. Swarm Intelligence, 4(4), 247-273. https://doi.org/10.1007/s11721-010-0045-5
This position paper explores the nature and role of two bio-inspired paradigms, namely Artificial Immune Systems (AIS) and Swarm Intelligence (SI). We argue that there are man...
On AIRS and clonal selection for machine learning
McEwan, C., & Hart, E. (2009)
On AIRS and clonal selection for machine learning. In Artificial Immune Systems. , (67-79). https://doi.org/10.1007/978-3-642-03246-2_11
Many recent advances have been made in understanding the functional implications of the global topological properties of biological networks through the application of complex...
Multi-Modal employee routing with time windows in an urban environment.
Urquhart, N. B., Hart, E., & Judson, A. (2015)
Multi-Modal employee routing with time windows in an urban environment. In Proceedings of the 2015 Genetic and Evolutionary Algorithms Conference, (1503-1504). https://doi.org/10.1145/2739482.2764649
An urban environment provides a number of challenges and opportunities
for organisations faced with the task of scheduling a mobile
workforce. Given a mixed set of public and ...
Representation in the (Artificial) Immune System
McEwan, C., & Hart, E. (2009)
Representation in the (Artificial) Immune System. Journal of Mathematical Modelling and Algorithms, 8, 125-149. https://doi.org/10.1007/s10852-009-9104-6
Much of contemporary research in Artificial Immune Systems (AIS) has partitioned into either algorithmic machine learning and optimisation, or, modelling biologically plausibl...
On the life-long learning capabilities of a NELLI*: a hyper-heuristic optimisation system.
Hart, E., & Sim, K. (2014)
On the life-long learning capabilities of a NELLI*: a hyper-heuristic optimisation system. In Proceedings of PPSN, 13th International Conference on Parallel problem Solving from Nature, (282-291). https://doi.org/10.1007/978-3-319-10762-2_28
Real-world applications of optimisation techniques place more importance on finding approaches that result in acceptable quality solutions in a short time-frame and can provid...
Learning to solve bin packing problems with an immune inspired hyper-heuristic.
Sim, K., Hart, E., & Paechter, B. (2013)
Learning to solve bin packing problems with an immune inspired hyper-heuristic. In P. Liò, O. Miglino, G. Nicosia, S. Nolfi, & M. Pavone (Eds.), Advances in Artificial Life, ECAL 2013, 856-863. https://doi.org/10.7551/978-0-262-31709-2-ch126
Motivated by the natural immune system's ability to defend the body by generating and maintaining a repertoire of antibodies that collectively cover the potential pathogen spa...