A histogram-matching approach to the evolution of bin-packing strategies

Abstract— We present a novel algorithm for the onedimension offline bin packing problem with discrete item sizes based on the notion of matching the item-size histogram with the bin-gap histogram. The approach is controlled by a constructive heuristic function which decides how to prioritise items in order to minimise the difference between histograms. We evolve such a function using a form of linear register-based genetic programming system. We test our evolved heuristics and compare them with hand-designed ones, including the wellknown best fit decreasing heuristic. The evolved heuristics are human-competitive, generally being able to outperform highperformance human-designed heuristics.

• Type: Conference
• Year: 2007
• Where: CEC
• Authors: Riccardo Poli, John Woodward, Edmund K. Burke
• Keywords: Artificial Intelligence, CEC 2007, Constructive Heuristic Function, Evolved Heuristics, Heuristics