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Automatic Design of Algorithms (ADA) treats algorithm choice and design as a machine learning problem, with problem instances as training data. However, this paper reveals that, as with classification and regression, for ADA not all training sets are equally valuable. We apply genetic programming ADA for bin packing to several new and existing benchmark sets. Using sets with narrowly-distributed features for training results in highly specialised algorithms, whereas those with well-spread features result in very general algorithms. Variance in certain features has a strong correlation with the generality of the trained policies.

Relating training instances to automatic design of algorithms for bin packing via features