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Genetic Algorithm on GPU Performance Optimization Issues

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Intelligent Data Engineering and Automated Learning – IDEAL 2013 (IDEAL 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8206))

Abstract

The aim of this paper is to investigate genetic algorithm execution on graphics processing unit performance issues and to develop techniques on how to speed up execution by optimizing algorithm execution path and data allocation. The paper presents methods to improve genetic algorithm performance by achieving higher hardware utilization and efficient task distribution between a graphics processing unit and a central processing unit.

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Author information

Authors and Affiliations

  1. Faculty of Mathematics and Informatics, Vilnius University, Vilnius, Lithuania

    Andrius Paukštė

Authors
  1. Andrius Paukštė
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

  2. University of Science and Technology of China, Hefei, China

    Ke Tang

  3. Nanjing University, Nanjing, China

    Yang Gao

  4. Ostfalia University of Applied Sciences, 38302, Wolfenbüttel, Germany

    Frank Klawonn

  5. Kyungpook National University, 702-701, Buk-Gu, Daegu, Korea

    Minho Lee

  6. Nature Inspired Computational and Applications Laboratory, School of Computer Science and Technology,, University of Science and Technology of China, 230027, Hefei, China

    Thomas Weise

  7. University of Science and Technology of China, 230017, Hefei, China

    Bin Li

  8. CERCIA, School of Computer Science, University of Birmingham, B15 2TT, Edgbaston, Birmingham, UK

    Xin Yao

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Paukštė, A. (2013). Genetic Algorithm on GPU Performance Optimization Issues. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_64

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  • DOI: https://doi.org/10.1007/978-3-642-41278-3_64

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41277-6

  • Online ISBN: 978-3-642-41278-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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