Dr Kevin Sim K.Sim@napier.ac.uk
Lecturer
Dr Kevin Sim K.Sim@napier.ac.uk
Lecturer
Prof Emma Hart E.Hart@napier.ac.uk
Professor
Prof Ben Paechter B.Paechter@napier.ac.uk
Professor
Pietro
Editor
Orazio Miglino
Editor
Giuseppe Nicosia
Editor
Stefano Nolfi
Editor
Mario Pavone
Editor
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 space, we describe an artificial system that discovers and maintains a repertoire of heuristics that collectively provide methods for solving problems within a problem space. Using bin-packing as an example domain, the system continuously generates novel heuristics represented using a tree-structure. An novel affinity measure provides stimulation between heuristics that cooperate by solving problems in different parts of the space. Using a test suite comprising of 1370 problem instances, we show that the system self-organises to a minimal repertoire of heuristics that provide equivalent performance on the test set to state-of-the art methods in hyper-heuristics. Moreover, the system is shown to be highly responsive and adaptive: it rapidly incorporates new heuristics both when entirely new sets of problem instances are introduced or when the problems presented change gradually over time.
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
Start Date | Sep 2, 2013 |
---|---|
End Date | Sep 6, 2013 |
Publication Date | 2013 |
Deposit Date | Aug 26, 2013 |
Publicly Available Date | Dec 31, 2013 |
Peer Reviewed | Peer Reviewed |
Pages | 856-863 |
Book Title | Advances in Artificial Life, ECAL 2013 |
DOI | https://doi.org/10.7551/978-0-262-31709-2-ch126 |
Keywords | Hyper-heuristics; artificial systems; problem solving; novel affinity measure; |
Public URL | http://researchrepository.napier.ac.uk/id/eprint/6251 |
Publisher URL | http://dx.doi.org/10.7551/978-0-262-31709-2-ch126 |
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