## 1 Introduction
In orderto overcome the above limits, initially we reduced the pixels density by applying binary masking to the dataset images after getting the minimized learning accuracy, we applied genetic algorithm to determine the binary mask that is to be applied to the dataset while training the model using CNN. It is demonstrated that the mask learning improves the training accuracy on different possible crossover methods. Numerical experiments are conducted for CNN learning on CS measurements using standard image classification datasets and a performance analysis was conducted. There are two works [20; 32] which are similar to our work and both used a sensing phenomenon mimicking the concept of SPC. SPC has a complicated implementation details but mathematically can be modeled using matrix operations. The sensing mechanism considered in our work is based on a standard digital camera output image and retaining only some selected pixels. This reduced set of pixels can be modeled as CS measurements [34]. Unlike SPC, the CS measurements we consider here is a simple spatial domain process which retains some pixel and force the remaining pixels to be zero. If there is a need to communicate the test image in an application platform it is enough to communicate the retained pixels as the mask is known both in client and server side. In server side CS measurements can be easily rearranged as an image using the mask pattern. In SPC-based sensing phenomenon the CS measurements cannot be directly used for testing or training. The inverse operation of SPC has to be performed to get the approximate images that are to be trained or tested [20; 32]. In our experiment, the key performance parameter we considered is the training accuracy of the CNN model for the given percentage of pixels used in the learning process. The percentage of the pixels not used is otherwise considered as the compression ratio CR. Considering the fact that the binary mask used are fixed for all the training and testing images and are arbitrarily high only in 10% of locations, we propose a Genetic Algorithm-based compressed learning (GACL) which is a novel process that will improve the training accuracy of the CNN. The GACL proposed is to determine the best mask that is to be applied to all images in the training set which will maximize the training accuracy of CNN. This is achieved by using Genetic Algorithm to determine the best chromosomes of the mask by assuming the CNN training accuracy as the objective function that is to be maximized. Our numerical experiment shows that in the process of CNN learning on CS measurements, when 10% of pixels are retained (compression ratio (CR) = 90%), the training accuracy of 77% with untrained fixed mask improves to 80% when the masks are genetically learned with vertical cross over and it drastically improves to 85% with diagonal crossover GA learning of mask. Comparison of the classification training accuracies of the CNN for two different scenarios, one with GACL and another without GACL, shows that GACL improves the model accuracies in a larger extent. This improvement performance is highly data dependent and can be compared only with existing work that uses the same sensing principle and dataset.