Speeding up the evaluation phase of GP classification algorithms on GPUs
Alberto CanoAmelia ZafraSebastian Ventura · 2012
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 com- pared to the sequential approach, reaching a speedup of up to 8209. Moreover, the model is able to scale to multiple GPU devices and can be easily extended to any evolu- tionary algorithm.