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Soft Computing

, Volume 15, Issue 8, pp 1657–1669 | Cite as

Graphics processing units and genetic programming: an overview

  • W. B. LangdonEmail author
Original Paper

Abstract

A top end graphics card (GPU) plus a suitable SIMD interpreter can deliver a several hundred fold speed up, yet cost less than the computer holding it. We give highlights of AI and computational intelligence applications in the new field of general purpose computing on graphics hardware (GPGPU). In particular, we surveyed genetic programming (GP) use with GPU. We gave several applications from Bioinformatics and showed that how the fastest GP is based on an interpreter rather than compilation. Finally using GP to generate GPU CUDA kernel C++ code is sketched.

Keywords

Genetic Programming Fitness Evaluation Multifactor Dimensionality Reduction Single Instruction Multiple Data Cartesian Genetic Programming 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

Work started in Memorial University, Newfoundland (with Wolfgang Banzhaf) and continued at Essex University and King’s College, London prior to UCL. Some of it using pre-release hardware donated by NVIDIA. I would like to thank Gernot Ziegler of NVIDIA and Lidia Yamamoto. Funded by EPSRC grant (http://gow.epsrc.ac.uk/ViewGrant.aspx?GrantRef=EP/G060525/2) EP/G060525/2

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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  1. 1.Department of Computer Science, CREST CentreUniversity CollegeLondonUK

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