A hyper-heuristic classifier for one dimensional bin packing problems: improving classification accuracy by attribute evolution

Authors:
Kevin Sim;Emma Hart;Ben Paechter
Affiliations:
Institute for Informatics and Digital Innovation, Edinburgh Napier University, Edinburgh, UK;Institute for Informatics and Digital Innovation, Edinburgh Napier University, Edinburgh, UK;Institute for Informatics and Digital Innovation, Edinburgh Napier University, Edinburgh, UK
Venue:
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
Year:
2012

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Abstract

A hyper-heuristic for the one dimensional bin packing problem is presented that uses an Evolutionary Algorithm (EA) to evolve a set of attributes that characterise a problem instance. The EA evolves divisions of variable quantity and dimension that represent ranges of a bin's capacity and are used to train a k-nearest neighbour algorithm. Once trained the classifier selects a single deterministic heuristic to solve each one of a large set of unseen problem instances. The evolved classifier is shown to achieve results significantly better than are obtained by any of the constituent heuristics when used in isolation.