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Speeding up the evaluation phase of GP classification algorithms on GPUs

Alberto Cano, Amelia Zafra, Sebastian Ventura
Department of Computing and Numerical Analysis, University of Cordoba, 14071 Cordoba, Spain
Soft Computing – A Fusion of Foundations, Methodologies and Applications, 2011

@article{cano2011speeding,

   title={Speeding up the evaluation phase of GP classification algorithms on GPUs},

   author={Cano, A. and Zafra, A. and Ventura, S.},

   journal={Soft Computing-A Fusion of Foundations, Methodologies and Applications},

   pages={1–16},

   publisher={Springer},

   year={2011}

}

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.
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