Fast prediction computation in learning classifier systems using CUDA (ACM)
Computing the system prediction is one of the most important and computationally expensive tasks in Learning Classifier Systems. In this paper, we provide a parallel solution to the problem of computing the prediction array in XCS using the NVIDIA‘s Compute Unified Device Architecture (CUDA). We performed several experiments to test our parallel solution using two different types of GPUs and to study how performances are affected by (i) the problem size, (ii) the number of problem actions, and (iii) the number of classifiers in the population. Our experimental results show a speedup that ranges from slightly less than 2X up to 32X.
Paper available at ACM.