Outputs (10)

Lifelong Learning Machines: Towards Developing Optimisation Systems That Continually Learn (2022)
Book Chapter
Hart, E. (2022). Lifelong Learning Machines: Towards Developing Optimisation Systems That Continually Learn. In A. E. Smith (Ed.), Women in Computational Intelligence: Key Advances and Perspectives on Emerging Topics (187-203). Springer. https://doi.org/1

Standard approaches to developing optimisation algorithms tend to involve selecting an algorithm and tuning it to work well on a large set of problem instances from the domain of interest. Once deployed, the algorithm remains static, failing to impro... Read More about Lifelong Learning Machines: Towards Developing Optimisation Systems That Continually Learn.

A Cross-Domain Method for Generation of Constructive and Perturbative Heuristics (2021)
Book Chapter
Stone, C., Hart, E., & Paechter, B. (2021). A Cross-Domain Method for Generation of Constructive and Perturbative Heuristics. In N. Pillay, & R. Qu (Eds.), Automated Design of Machine Learning and Search Algorithms (91-107). Springer. https://doi.org/10.1

Hyper-heuristic frameworks, although intended to be cross-domain at the highest level, usually rely on a set of domain-specific low-level heuristics which exist below the domain-barrier and are manipulated by the hyper-heuristic itself. However, for... Read More about A Cross-Domain Method for Generation of Constructive and Perturbative Heuristics.

Towards Autonomous Robot Evolution (2020)
Book Chapter
Eiben, A. E., Hart, E., Timmis, J., Tyrrell, A. M., & Winfield, A. F. (2021). Towards Autonomous Robot Evolution. In A. Cavalcanti, B. Dongol, R. Hierons, J. Timmis, & J. Woodcock (Eds.), Software Engineering for Robotics (29-51). Springer. https://doi.or

We outline a perspective on the future of evolutionary robotics and discuss a long-term vision regarding robots that evolve in the real world. We argue that such systems offer significant potential for advancing both science and engineering. For scie... Read More about Towards Autonomous Robot Evolution.

Biological Networks (2011)
Book Chapter
Hart, E. (2011). Biological Networks. In M. Gargaud, R. Amils, J. Quintanilla, & H. Irvine (Eds.), Encyclopedia of Astrobiology (179-182). Springer. https://doi.org/10.1007/978-3-642-11274-4_178

Exploiting Collaborations in the Immune System: The Future of Artificial Immune Systems (2009)
Book Chapter
Hart, E., McEwan, C., & Davoudani, D. (2009). Exploiting Collaborations in the Immune System: The Future of Artificial Immune Systems. In C. Mumford, & L. Jain (Eds.), Intelligent Systems Reference Library; Computational Intelligence (527-558). Springer-V

Despite a steady increase in the application of algorithms inspired by the natural immune system to a variety of domains over the previous decade, we argue that the field of Artificial Immune Systems has yet to achieve its full potential. We suggest... Read More about Exploiting Collaborations in the Immune System: The Future of Artificial Immune Systems.

An overview of artificial immune systems. (2005)
Book Chapter
Timmis, J., Knight, T., de Castro, L., & Hart, E. (2005). An overview of artificial immune systems. In R. Paton, H. Bolouri, W. M. L. Holcombe, J. H. Parish, & R. Tateson (Eds.), Computation in Cells and Tissues: Perspectives and Tools of Thought (51-86).

The immune system is highly distributed, highly adaptive, self-organising in nature, maintains a memory of past encounters and has the ability to continually learn about new encounters. From a computational point of view, the immune system has much t... Read More about An overview of artificial immune systems..

Hyper Heuristics: an emerging direction in modern search technology. (2003)
Book Chapter
Burke, E., Hart, E., Kendall, G., Newall, J., Ross, P., & Schulenburg, S. (2003). Hyper Heuristics: an emerging direction in modern search technology. In F. Glover, & G. A. Kochenberger (Eds.), Handbook of MetaHeuristics (457-474). Springer US. https://do

Genetic algorithms and timetabling (2003)
Book Chapter
Ross, P., Hart, E., & Corne, D. (2003). Genetic algorithms and timetabling. In A. Ghosh, & K. Tsutsui (Eds.), Advances in Evolutionary Optimisation. Springer. https://doi.org/10.1007/978-3-642-18965-4_30

Genetic algorithms can be used to search very large spaces, and it would seem natural to use them for tackling the nastier kinds of timetabling problem. We completed an EPSRC-funded project on this last year, and distribute a free package that handle... Read More about Genetic algorithms and timetabling.

Combining choices of heuristics. (2002)
Book Chapter
Ross, P., & Hart, E. (2002). Combining choices of heuristics. In R. Sarker, M. Mohammadian, & X. Yao (Eds.), Evolutionary Optimization (229-252). Kluwer

The evolution and analysis of a potential antibody library for use in job-shop scheduling (1999)
Book Chapter
Hart, E., & Ross, P. (1999). The evolution and analysis of a potential antibody library for use in job-shop scheduling. In D. Corne, M. Dorigo, F. Glover, D. Dasgupta, P. Moscato, R. Poli, & K. V. Price (Eds.), New Ideas in Optimisation (185-202). McGraw-