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Generic Local Search (Memetic) Algorithm on a Single GPGPU Chip

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Massively Parallel Evolutionary Computation on GPGPUs

Part of the book series: Natural Computing Series ((NCS))

Abstract

Memetic algorithms (MAs), evolutionary algorithms coupled with a local search routine, have been shown to be very efficient in solving a great variety of problems. This chapter presents the first implementation of a generic parallel MA on a general-purpose graphics processing unit card. An upgrade of the EASEA platform provides an automatic generation and parallelization of an MA for both novice and experienced users. Experiments on a benchmark function and a real-world problem reveal speedups ranging between × 70 and × 120, depending on population size and number of local search iterations.

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Notes

  1. 1.

    http://easea.unistra.fr

  2. 2.

    www.gefore.com/Hardware/GPUs/gefore-gtx-295

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Author information

Authors and Affiliations

  1. ICUBE, University of Strasbourg, Illkirch, France

    Frédéric Krüger, Ogier Maitre & Pierre Collet

  2. Insituto de Tecnologia Quimica, UPV-CSIC, Valencia, Spain

    Santiago Jiménez & Laurent A. Baumes

Authors
  1. Frédéric Krüger
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  2. Ogier Maitre
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  3. Santiago Jiménez
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  4. Laurent A. Baumes
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  5. Pierre Collet
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Corresponding author

Correspondence to Frédéric Krüger .

Editor information

Editors and Affiliations

  1. and Information Science, Hannan University Dept. of Management, Matsubara, Osaka, Japan

    Shigeyoshi Tsutsui

  2. ICube Laboratory UMR CNRS 7357, Université de Strasbourg, Illkirch, France

    Pierre Collet

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Krüger, F., Maitre, O., Jiménez, S., Baumes, L.A., Collet, P. (2013). Generic Local Search (Memetic) Algorithm on a Single GPGPU Chip. In: Tsutsui, S., Collet, P. (eds) Massively Parallel Evolutionary Computation on GPGPUs. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37959-8_4

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  • DOI: https://doi.org/10.1007/978-3-642-37959-8_4

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37958-1

  • Online ISBN: 978-3-642-37959-8

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