Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Evolving Market Index Trading Rules Using Grammatical Evolution
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
GPU Gems 2: Programming Techniques for High-Performance Graphics and General-Purpose Computation (Gpu Gems)
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
Gpu gems 3
GEVA: grammatical evolution in Java
ACM SIGEVOlution
Shape grammars and grammatical evolution for evolutionary design
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Evolutionary optimization of multistage interconnection networks performance
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Structural and nodal mutation in grammatical evolution
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Foundations in Grammatical Evolution for Dynamic Environments
Foundations in Grammatical Evolution for Dynamic Environments
Fast genetic programming on GPUs
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
A SIMD interpreter for genetic programming on GPU graphics cards
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Grammar-based Genetic Programming: a survey
Genetic Programming and Evolvable Machines
Programming Massively Parallel Processors: A Hands-on Approach
Programming Massively Parallel Processors: A Hands-on Approach
Evolving a ms. pacman controller using grammatical evolution
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Parallel genetic algorithm on the CUDA architecture
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
IEEE Transactions on Evolutionary Computation
Distilling GeneChips with GP on the emerald GPU supercomputer
ACM SIGEVOlution
On GPU based fitness evaluation with decoupled training partition cardinality
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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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. 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 PCI-express 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.