Speeding up the evaluation phase of GP classification algorithms on GPUs

Authors:
Alberto Cano;Amelia Zafra;Sebastián Ventura
Affiliations:
University of Córdoba, Department of Computing and Numerical Analysis, 14071, Córdoba, Spain;University of Córdoba, Department of Computing and Numerical Analysis, 14071, Córdoba, Spain;University of Córdoba, Department of Computing and Numerical Analysis, 14071, Córdoba, Spain
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary Computation on General Purpose Graphics Processing Units
Year:
2012

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Abstract

The efficiency of evolutionary algorithms has become a studied problem since it is one of the major weaknesses in these algorithms. Specifically, when these algorithms are employed for the classification task, the computational time required by them grows excessively as the problem complexity increases. This paper proposes an efficient scalable and massively parallel evaluation model using the NVIDIA CUDA GPU programming model to speed up the fitness calculation phase and greatly reduce the computational time. Experimental results show that our model significantly reduces the computational time compared to the sequential approach, reaching a speedup of up to 820×. Moreover, the model is able to scale to multiple GPU devices and can be easily extended to any evolutionary algorithm.