Creating optimised employee travel plans
Urquhart, N., & Hart, E. (2019)
Creating optimised employee travel plans. In Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences, (489-502). https://doi.org/10.1007/978-3-319-89988-6_29
The routing of employees who provide services such as home health or social care is a complex problem. When sending an employee between two addresses , there may exist more th...
For Flux Sake: The Confluence of Socially- and Biologically-Inspired Computing for Engineering Change in Open Systems
Pitt, J., & Hart, E. (2017)
For Flux Sake: The Confluence of Socially- and Biologically-Inspired Computing for Engineering Change in Open Systems. In 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems (FAS*W),https://doi.org/10.1109/fas-w.2017.119
This position paper is concerned with the challenge of engineering multi-scale and long-lasting systems, whose operation is regulated by sets of mutually-agreed, conventional ...
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...
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 ...
Revisiting the Central and Peripheral Immune System
McEwan, C., Hart, E., & Paechter, B. (2006)
Revisiting the Central and Peripheral Immune System. In Artificial Immune Systems. ICARIS 2007, 240-251. doi:10.1007/978-3-540-73922-7_21
The idiotypic network has a long and chequered history in both theoretical immunology and Artificial Immune Systems. In terms of the latter, the drive for engineering applicat...
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...
Exploiting the analogy between immunology and sparse distributed memory.
Hart, E., & Ross, P. (2001)
Exploiting the analogy between immunology and sparse distributed memory. In J. Timmis, & P. J. Bentley (Eds.), ICARIS 2002 : 1st International Conference on Artificial Immune Systems, 59-67
The relationship between immunological memory and a class of associative memories known as sparse distributed memories (SDM) is well known. This paper proposes a new model for...
Grid diversity operator for some population-based optimization algorithms.
Salah, A., & Hart, E. (2015)
Grid diversity operator for some population-based optimization algorithms. In Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference - GECCO Companion '15, (1475-1476). https://doi.org/10.1145/2739482.2764664
We present a novel diversity method named Grid Diversity
Operator (GDO) that can be incorporated into multiple
population-based optimization algorithms that guides the