Conference paperPOSPÍCHAL Petr, SCHWARZ Josef and JAROŠ Jiří. Acceleration of grammatical evolution using graphics processing units: computational intelligence on consumer games and graphics hardware. In: Proceedings of the 2011 GECCO conference companion on Genetic and evolutionary computation. New York: Association for Computing Machinery, 2011, pp. 431439. ISBN 9781450306904.  Publication language:  english 

Original title:  Acceleration of grammatical evolution using graphics processing units: computational intelligence on consumer games and graphics hardware 

Title (cs):  Akcelerace gramatické evoluce s použitím grafických čipů 

Pages:  431439 

Proceedings:  Proceedings of the 2011 GECCO conference companion on Genetic and evolutionary computation 

Conference:  Genetic and Evolutionary Computation Conference 2011 

Place:  New York, US 

Year:  2011 

ISBN:  9781450306904 

Publisher:  Association for Computing Machinery 

Keywords 

CUDA, grammatical evolution, graphics chips, GPU, GPGPU, speedup, symbolic regression 
Annotation 

Several papers show that symbolic regression is suitable for data analysis and prediction in financial markets. Grammatical Evolution (GE), a grammarbased form of Genetic Programming (GP), has been successfully applied in solving various tasks including symbolic regression. However, often the computational effort to calculate the fitness of a solution in GP can limit the area of possible application and/or the extent of experimentation undertaken.
This paper deals with utilizing mainstream graphics processing units (GPU) for acceleration of GE solving symbolic regression. GPU optimization details are discussed and the NVCC compiler is analyzed. We design an effective mapping of the algorithm to the CUDA framework, and in so doing must tackle constraints of the GPU approach, such as the PCIexpress bottleneck and main memory transactions.
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 utilized, GPU can offer an interesting performance boost for GE tackling symbolic regression. 
BibTeX: 

@INPROCEEDINGS{
author = {Petr Posp{\'{i}}chal and Josef Schwarz and Ji{\v{r}}{\'{i}}
Jaro{\v{s}}},
title = {Acceleration of grammatical evolution using graphics
processing units: computational intelligence on consumer
games and graphics hardware},
pages = {431439},
booktitle = {Proceedings of the 2011 GECCO conference companion on
Genetic and evolutionary computation},
year = {2011},
location = {New York, US},
publisher = {Association for Computing Machinery},
ISBN = {9781450306904},
language = {english},
url = {http://www.fit.vutbr.cz/research/view_pub.php?id=9791}
} 
