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Speeding up Genetic Programming Based Symbolic Regression Using GPUs

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PRICAI 2022: Trends in Artificial Intelligence (PRICAI 2022)

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Abstract

Symbolic regression has multiple applications in data mining and scientific computing. Genetic Programming (GP) is the mainstream method of solving symbolic regression problems, but its execution speed under large datasets has always been a bottleneck. This paper describes a CUDA-based parallel symbolic regression algorithm that leverages the parallelism of the GPU to speed up the fitness evaluation process in symbolic regression. We make the fitness evaluation step fully performed on the GPU and make use of various GPU hardware resources. We compare training time and regression accuracy between the proposed approach and existing symbolic regression frameworks including gplearn, TensorGP, and KarooGP. The proposed approach is the fastest among all the tested frameworks in both synthetic benchmarks and large-scale benchmarks.

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Authors and Affiliations

  1. Sichuan University, Chengdu, 610000, China

    Rui Zhang & Yanan Sun

  2. Victoria University of Wellington, Wellington, 6140, New Zealand

    Andrew Lensen

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  1. Rui Zhang
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  2. Andrew Lensen
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  3. Yanan Sun
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Correspondence to Yanan Sun .

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Editors and Affiliations

  1. CSIRO Australian e-Health Research Centre, Brisbane, QLD, Australia

    Sankalp Khanna

  2. Shanghai Jiao Tong University, Shanghai, China

    Jian Cao

  3. University of Tasmania, Hobart, TAS, Australia

    Quan Bai

  4. University of Technology Sydney, Sydney, NSW, Australia

    Guandong Xu

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Zhang, R., Lensen, A., Sun, Y. (2022). Speeding up Genetic Programming Based Symbolic Regression Using GPUs. In: Khanna, S., Cao, J., Bai, Q., Xu, G. (eds) PRICAI 2022: Trends in Artificial Intelligence. PRICAI 2022. Lecture Notes in Computer Science, vol 13629. Springer, Cham. https://doi.org/10.1007/978-3-031-20862-1_38

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  • DOI: https://doi.org/10.1007/978-3-031-20862-1_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20861-4

  • Online ISBN: 978-3-031-20862-1

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