The idea behind hyper-heuristics is to discover rules that relate different problem states with the best single heuristic to apply. This investigation works towards extending the problem domain in which a given hyper-heuristic can be applied and implements a framework to generate hyper-heuristics for a wider range of bin packing problems. We present a GA-based method that produces general hyper-heuristics that solve a variety of instances of one- and two dimensional bin packing problem without further parameter tuning. The two-dimensional problem instances considered deal with rectangles, convex and non-convex polygons.
['Eunice López-Camacho', 'Hugo Terashima-Marín', 'Peter Ross']
A hyper-heuristic for solving one and two-dimensional bin packing problems