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Exploiting Tournament Selection for Efficient Parallel Genetic Programming

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Advances in Computational Intelligence Systems (UKCI 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 840))

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Abstract

Genetic Programming (GP) is a computationally intensive technique which is naturally parallel in nature. Consequently, many attempts have been made to improve its run-time from exploiting highly parallel hardware such as GPUs. However, a second methodology of improving the speed of GP is through efficiency techniques such as subtree caching. However achieving parallel performance and efficiency is a difficult task. This paper will demonstrate an efficiency saving for GP compatible with the harnessing of parallel CPU hardware by exploiting tournament selection. Significant efficiency savings are demonstrated whilst retaining the capability of a high performance parallel implementation of GP. Indeed, a 74% improvement in the speed of GP is achieved with a peak rate of 96 billion GPop/s for classification type problems.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Bristol, Merchant Venturers Bldg, Woodland Road, Bristol, BS8 1UB, UK

    Darren M. Chitty

Authors
  1. Darren M. Chitty
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Correspondence to Darren M. Chitty .

Editor information

Editors and Affiliations

  1. School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom

    Ahmad Lotfi

  2. Faculty of Science and Technology, Bournemouth University, Poole, Dorset, United Kingdom

    Hamid Bouchachia

  3. School of Computing, University of Portsmouth, Portsmouth, Hampshire, United Kingdom

    Alexander Gegov

  4. School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom

    Caroline Langensiepen

  5. College of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom

    Martin McGinnity

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Chitty, D.M. (2019). Exploiting Tournament Selection for Efficient Parallel Genetic Programming. In: Lotfi, A., Bouchachia, H., Gegov, A., Langensiepen, C., McGinnity, M. (eds) Advances in Computational Intelligence Systems. UKCI 2018. Advances in Intelligent Systems and Computing, vol 840. Springer, Cham. https://doi.org/10.1007/978-3-319-97982-3_4

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