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: (Ed.) Proceedings of PPSN, 13th International Conference on Parallel problem Solving from Nature, , () ( ed.). (pp. ). : . Springer.


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Abstract

Real-worldapplicationsofoptimisationtechniquesplacemore importance on finding approaches that result in acceptable quality solu- tions in a short time-frame and can provide robust solutions, capable of being modified in response to changes in the environment than seeking elusive global optima. We demonstrate that a hyper-heuristic approach NELLI* that takes inspiration from artifical immune systems is capa- ble of life-long learning in an environment where problems are presented in a continuous stream and change over time. Experiments using 1370 bin-packing problems show excellent performance on unseen problems and that the system maintains memory, enabling it to exploit previously learnt heuristics to solve new problems with similar characteristics to ones solved in the past.
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Authors

Emma Hart
Director of CEC
e.hart@napier.ac.uk
+44 131 455 2783
Kevin Sim
Lecturer
K.Sim@napier.ac.uk
+44 131 455 2497

Areas of Expertise

Bio-inspired Computing
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.