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

Sim, Kevin; Hart, Emma; Paechter, Ben

Abstract

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.

Citation

Sim, K., Hart, E., & Paechter, B. (2013, September). Learning to solve bin packing problems with an immune inspired hyper-heuristic

• 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
• Contract Date: Aug 26, 2013