policy matrix evolution for generation of heuristics
Online bin-packing is a well-known problem in which immediate decisions must be made about the placement of items with various sizes into fixed capacity bins. The associated decisions can be based on an index policy in which each decision option is independently given a value and the highest value choice is selected. In this paper, we represent such heuristics for online bin packing as a simple matrix of scores. We then use a genetic algorithm to search for matrices giving good performance. This might be regarded as parameter tuning of the packing heuristic but in which a fine-grained representation is used and so the number of parameters is much larger than in standard parameter tuning. The evolved matrices perform better than the standard heuristics. They also reveal interesting structures and so have impact on questions of how heuristic score functions should be represented and what structure they might be expected to exhibit.
heuristics index policies metaheuristics online problems hyper-heuristics