Implementing cartesian genetic programming classifiers on graphics processing units using GPU.NET
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}
}
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.
September 12, 2011 by hgpu
