Abstract
This paper presents an implementation of Grammatical Evolution on a GPU architecture. Our proposal, Embedded Grammars, implements the grammar directly in the code. Although more rigid, it allows to compute the decodification in parallel with the evaluation of the individuals. We tested three different grammars with a set of eight symbolic regression problems. The symbolic regression problems consists on obtaining a mathematical expression in the form \(y=f(x)\), in our case, from a set of 288 pairs x, y. The analysis of the results shows that Embedded Grammars are better not only in terms of execution time, but also in quality when compared with an implementation on a CPU. Speed-up results are also better than those presented in the literature.
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Acknowledgements
This work was supported by the Spanish Government Minister of Science and Innovation under grants TIN2014-54806-R, TIN2015-65277-R and CAPAP-H5 network (TIN2014-53522) and TIN2015-65460-C2. J.I. Hidalgo also acknowledges the support of the Spanish Ministry of Education mobility grant PRX16/00216.
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Hidalgo, J.I., Cervigón, C., Velasco, J.M., Colmenar, J.M., García-Sánchez, C., Botella, G. (2017). Embedded Grammars for Grammatical Evolution on GPGPU. In: Squillero, G., Sim, K. (eds) Applications of Evolutionary Computation. EvoApplications 2017. Lecture Notes in Computer Science(), vol 10199. Springer, Cham. https://doi.org/10.1007/978-3-319-55849-3_51
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