Genetic algorithms (GA) provide an efficient method for training filters to find proper weights using a fitness function where the input signal is filtered and compared with the desired output. In the case of image processing applications, the high computational cost of the fitness function that is evaluated repeatedly can cause training time to be relatively long. In this study, a new algorithm, called sub-image blocks based on graphical processing units (GPU), is developed to accelerate the training of mask weights using GA. The method is developed by discussing other alternative design considerations, including direct method (DM), population-based method (PBM), block-based method (BBM), and sub-images-based method (SBM). A comparative performance evaluation of the introduced methods is presented using sequential and other GPUs. Among the discussed designs, SBM provides the best performance by taking advantage of the block shared and thread local memories in GPU. According to execution duration and comparative acceleration graphs, SBM provides approximately 55–90 times more acceleration using GeForce GTX 660 over sequential implementation on a 3.5GHz processor. 
GPU accelerated training of image convolution filter weights using genetic algorithms