We present an implementation of genetic algorithm (GA) training of feedforward artificial neural networks (ANNs) targeting commodity graphics cards (GPUs). By carefully mapping the problem onto the unique GPU architecture, we achieve order-of-magnitude speedup over a conventional CPU implementation. Furthermore, we show that the speedup is consistent across a wide range of data set sizes, making this implementation ideal for large data sets. This performance boost enables the genetic algorithm to search a larger subset of the solution space, which results in more accurate pattern classification. Finally, we demonstrate this method in the context of the 2009 UC San Diego Data Mining Contest, achieving a world-class lift on a data set of 94682 e-commerce transactions.

Additional Metadata
Persistent URL dx.doi.org/10.1088/1742-6596/256/1/012014
Journal Journal of Physics: Conference Series
Citation
Patulea, C. (Catalin), Peace, R. (Robert), & Green, J. (2010). CUDA-accelerated genetic feedforward-ANN training for data mining. In Journal of Physics: Conference Series (Vol. 256). doi:10.1088/1742-6596/256/1/012014