  We propose a novel technique for algorithm-selection, applicable to
optimisation domains in which there is implicit sequential information
encapsulated in the data, e.g., in online bin-packing. Specifically we train
two types of recurrent neural networks to predict a packing heuristic in online
bin-packing, selecting from four well-known heuristics. As input, the RNN
methods only use the sequence of item-sizes. This contrasts to typical
approaches to algorithm-selection which require a model to be trained using
domain-specific instance features that need to be first derived from the input
data. The RNN approaches are shown to be capable of achieving within 5% of the
oracle performance on between 80.88% to 97.63% of the instances, depending on
the dataset. They are also shown to outperform classical machine learning
models trained using derived features. Finally, we hypothesise that the
proposed methods perform well when the instances exhibit some implicit
structure that results in discriminatory performance with respect to a set of
heuristics. We test this hypothesis by generating fourteen new datasets with
increasing levels of structure, and show that there is a critical threshold of
structure required before algorithm-selection delivers benefit.

Machine Learning;Artificial Intelligence;Neural and Evolutionary Computing