Research Output
Algorithm selection using deep learning without feature extraction
  We propose a novel technique for algorithm-selection which adopts a deep-learning approach, specifically a Recurrent-Neural Network with Long-Short-Term-Memory (RNN-LSTM). In contrast to the majority of work in algorithm-selection, the approach does not need any features to be extracted from the data but instead relies on the temporal data sequence as input. A large case-study in the domain of 1-d bin packing is undertaken in which instances can be solved by one of four heuristics. We first evolve a large set of new problem instances that each have a clear "best solver" in terms of the heuristics considered. An RNN-LSTM is trained directly using the sequence data describing each instance to predict the best performing heuristic. Experiments conducted on small and large problem instances with item sizes generated from two different probability distributions are shown to achieve between 7% to 11% improvement over the single best solver (SBS) (i.e. the single heuristic that achieves the best performance over the instance set) and 0% to 2% lower than the virtual best solver (VBS), i.e the perfect mapping.

  • Date:

    13 July 2019

  • Publication Status:


  • DOI:


  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    006 Special Computer Methods

  • Funders:

    Edinburgh Napier Funded


Alissa, M., Sim, K., & Hart, E. (2019). Algorithm selection using deep learning without feature extraction. In GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion. , (198-206).



Deep Learning, Recurrent Neural Network, Algorithm Selection

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