On the life-long learning capabilities of a NELLI*: a hyper-heuristic optimisation system.

Hart, Emma; Sim, Kevin

Abstract

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 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 capable 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.

Citation

Hart, E., & Sim, K. (2014, September). On the life-long learning capabilities of a NELLI*: a hyper-heuristic optimisation system

• Presentation Conference Type: Conference Paper (published)
• Start Date: Sep 13, 2014
• End Date: Sep 17, 2014
• Publication Date: 2014
• Deposit Date: Jul 1, 2014
• Publicly Available Date: Dec 31, 2014
• Peer Reviewed: Peer Reviewed
• Volume: 8672
• Pages: 282-291
• Book Title: Proceedings of PPSN, 13th International Conference on Parallel problem Solving from Nature
• ISBN: 978-3-319-10761-5
• DOI: https://doi.org/10.1007/978-3-319-10762-2_28
• Keywords: Real-world optimisation; hyper-heuristics; NELLI; artificial immune systems
• Public URL: http://researchrepository.napier.ac.uk/id/eprint/6902
• Contract Date: Jul 1, 2014