Created by W.Langdon from gp-bibliography.bib Revision:1.7954
This is the first occasion GE has been adapted for running on a GPU. We measure our implementation running on one core of CPU Core i7 and GPU GTX 480 together with a GE library written in JAVA, GEVA.
Results indicate that our algorithm offers the same convergence, and it is suitable for a larger number of regression points where GPU is able to reach speedups of up to 39 times faster when compared to GEVA on a serial CPU code written in C. In conclusion, properly used, GPU can offer an interesting performance boost for GE tackling symbolic regression.",
Also known as \cite{2002030} Distributed on CD-ROM at GECCO-2011.
ACM Order Number 910112.",
Genetic Programming entries for Petr Pospichal Eoin Murphy Michael O'Neill Josef Schwarz Jiri Jaros