  Self-supervised Learning (SSL) is a machine learning algorithm for
pretraining Deep Neural Networks (DNNs) without requiring manually labeled
data. The central idea of this learning technique is based on an auxiliary
stage aka pretext task in which labeled data are created automatically through
data augmentation and exploited for pretraining the DNN. However, the effect of
each pretext task is not well studied or compared in the literature. In this
paper, we study the contribution of augmentation operators on the performance
of self supervised learning algorithms in a constrained settings. We propose an
evolutionary search method for optimization of data augmentation pipeline in
pretext tasks and measure the impact of augmentation operators in several SOTA
SSL algorithms. By encoding different combination of augmentation operators in
chromosomes we seek the optimal augmentation policies through an evolutionary
optimization mechanism. We further introduce methods for analyzing and
explaining the performance of optimized SSL algorithms. Our results indicate
that our proposed method can find solutions that outperform the accuracy of
classification of SSL algorithms which confirms the influence of augmentation
policy choice on the overall performance of SSL algorithms. We also compare
optimal SSL solutions found by our evolutionary search mechanism and show the
effect of batch size in the pretext task on two visual datasets.
