5542

Implementing cartesian genetic programming classifiers on graphics processing units using GPU.NET

Simon Harding, Wolfgang Banzhaf
IDSIA, Lugano, Switzerland & Memorial University, St John’s, Canada
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, GECCO ’11, 2011

@inproceedings{harding2011implementing,

   title={Implementing cartesian genetic programming classifiers on graphics processing units using GPU. NET},

   author={Harding, S. and Banzhaf, W.},

   booktitle={Proceedings of the 13th annual conference companion on Genetic and evolutionary computation},

   pages={463–470},

   year={2011},

   organization={ACM}

}

Source Source   

703

views

This paper investigates the use of a new Graphics Processing Unit (GPU) programming tool called ‘GPU.NET’ for implementing a Genetic Programming fitness evaluator. We find that the tool is able to help write software that accelerates fitness evaluation. For the first time, Cartesian Genetic Programming (CGP) was used with a GPU-based interpreter. With its code reuse and compact representation, implementing CGP efficiently on the GPU required several innovations. Further, we tested the system on a very large data set, and showed that CGP is also suitable for use as a classifier.
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2017 hgpu.org

All rights belong to the respective authors

Contact us: