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Learning to solve bin packing problems with an immune inspired hyper-heuristic.

Sim, Kevin; Hart, Emma; Paechter, Ben




Orazio Miglino

Giuseppe Nicosia

Stefano Nolfi

Mario Pavone


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).

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
Keywords Hyper-heuristics; artificial systems; problem solving; novel affinity measure;
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