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Algorithm selection using deep learning without feature extraction

Alissa, Mohamad; Sim, Kevin; Hart, Emma

Authors

Mohamad Alissa



Abstract

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.

Citation

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). https://doi.org/10.1145/3321707.3321845

Conference Name Genetic and Evolutionary Computing Conference (GECCO) 2019
Conference Location Prague, Czech Republic
Start Date Jul 13, 2019
End Date Jul 17, 2019
Acceptance Date Mar 21, 2019
Publication Date Jul 13, 2019
Deposit Date Apr 16, 2019
Publicly Available Date Mar 29, 2024
Publisher Association for Computing Machinery (ACM)
Pages 198-206
Book Title GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion
ISBN 978-1-4503-6748-6
DOI https://doi.org/10.1145/3321707.3321845
Keywords Deep Learning, Recurrent Neural Network, Algorithm Selection
Public URL http://researchrepository.napier.ac.uk/Output/1732820

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