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.

本文介绍了一种新颖的算法选择技术，其中借助循环神经网络在在线装箱问题中预测打包启发式，表现优于传统机器学习方法，并提出了这种算法的表现好处需要数据具备一定隐式结构的假设。