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DOI: http://dx.doi.org/10.7551/978-0-262-31709-2-ch126
Pages 856-863
First published 2 September 2013

Learning to Solve Bin Packing Problems with an Immune Inspired Hyper-heuristic

Kevin Sim, Emma Hart, Ben Paechter

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.