Genetic Algorithm and Binary Masks for Co-Learning Multiple Dataset in Deep Neural Networks

This study addresses the challenges of 'catastrophic forgetting' and 'multi-task learning' encountered in the field of data classification and analysis, particularly with the use of Convolutional Neural Networks (CNNs). The aim of the study is to employ genetic algorithm (GA) to mitigate these issues. Methodologically, we have developed an optimization strategy that utilizes layer-based binary masks to tailor CNNs models for multiple dataset. GA serves as a heuristic search method to optimize a binary mask for each dataset. Experiments have been conducted on widely-used dataset such as MNIST, Fashion MNIST, and KMNIST. The obtained results are notably impressive, yielding classification accuracies of 76.25% for MNIST, 76% for Fashion MNIST, and 74.43% for KMNIST. These findings demonstrate that our proposed approach can generate high-performance models not only for a single task but also for multiple tasks.