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Massively Parallel Evolutionary Computation on GPGPUs pp 227–248Cite as

Genetic Programming on GPGPU Cards Using EASEA

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Abstract

Genetic programming is one of the most powerful evolutionary paradigms because it allows us to optimize not only the parameter space but also the structure of a solution. The search space explored by genetic programming is therefore huge and necessitates a very large computing power which is exactly what GPGPUs can provide. This chapter will show how Koza-like tree-based genetic programming can be efficiently ported onto GPGPU processors.

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Authors and Affiliations

  1. LSIIT, University of Strasbourg, Pôle API, Bd Sébastien Brant, 10413, 67412, Illkirch, France

    Ogier Maitre

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  1. Ogier Maitre
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Correspondence to Ogier Maitre .

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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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Maitre, O. (2013). Genetic Programming on GPGPU Cards Using EASEA. 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_11

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  • DOI: https://doi.org/10.1007/978-3-642-37959-8_11

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37958-1

  • Online ISBN: 978-3-642-37959-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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