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Cartesian Genetic Programming on the GPU

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Massively Parallel Evolutionary Computation on GPGPUs

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

Abstract

Cartesian Genetic Programming is a form of Genetic Programming based on evolving graph structures. It has a fixed genotype length and a genotype–phenotype mapping that introduces neutrality into the representation. It has been used for many applications and was one of the first Genetic Programming techniques to be implemented on the GPU. In this chapter, we describe the representation in detail and discuss various GPU implementations of it. Later in the chapter, we discuss a recent implementation based on the GPU.net framework.

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Acknowledgements

SH acknowledges the funding from Atlantic Canada’s HPC network ACENET and by NSERC under the Discovery Grant Program RGPIN 283304-07. SH would like to thank Innervision Medical Ltd. and TidePowerd for their technical assistance.

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Authors and Affiliations

  1. Machine Intelligence Ltd, Exeter, UK, EX4 IEJ

    Simon Harding

  2. Department of Electronics, University of York, York, Y01 9UD, UK

    Julian F. Miller

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  1. Simon Harding
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  2. Julian F. Miller
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Correspondence to Simon Harding .

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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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Harding, S., Miller, J.F. (2013). Cartesian Genetic Programming on the GPU. 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_12

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  • DOI: https://doi.org/10.1007/978-3-642-37959-8_12

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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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