Implementing cartesian genetic programming classifiers on graphics processing units using GPU.NET
Simon Peter Harding, Wolfgang Banzhaf · 2011
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.