"P(GAP103/10/1517), S, Z(MSM0021630528)" . "2011-07-12+02:00"^^ . . . "Proceedings of the 2011 GECCO conference companion on Genetic and evolutionary computation" . "978-1-4503-0690-4" . . "Several papers show that symbolic regression is suitable for data analysis and prediction in financial markets. Grammatical Evolution (GE), a grammar-based 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.\u00A0 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. \u00A0We 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 PCI-express bottleneck and main memory transactions.\u00A0 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"@en . . "8"^^ . "New York" . . . "26230" . "RIV/00216305:26230/11:PU96181!RIV13-MSM-26230___" . . . "[85C5194DD8B9]" . "Acceleration of Grammatical Evolution Using Graphics Processing Units"@en . . . . . . "Acceleration of Grammatical Evolution Using Graphics Processing Units" . . . . "Dublin" . "184380" . "Jaro\u0161, Ji\u0159\u00ED" . . . "RIV/00216305:26230/11:PU96181" . . "Acceleration of Grammatical Evolution Using Graphics Processing Units" . . "Association for Computing Machinery" . "CUDA, grammatical evolution, graphics chips, GPU, GPGPU, speedup, symbolic regression"@en . "3"^^ . "Acceleration of Grammatical Evolution Using Graphics Processing Units"@en . . "Schwarz, Josef" . "3"^^ . "Several papers show that symbolic regression is suitable for data analysis and prediction in financial markets. Grammatical Evolution (GE), a grammar-based 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.\u00A0 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. \u00A0We 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 PCI-express bottleneck and main memory transactions.\u00A0 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" . "Posp\u00EDchal, Petr" . . . . . .