Optimization of deep learning algorithms for large digital data processing using evolutionary neural networks

Abstract

This paper introduces a unique method for boosting the efficiency of deep learning algorithms in processing large amounts of virtual facts. This approach leverages evolutionary neural networks, integrating deep mastering algorithms with evolutionary algorithms to enhance the overall performance of convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The proposed optimization technique employs evolutionary operators such as natural choice, version aggregate, and random weight mutations to discover massive and complicated seek areas. The innovation of this studies lies inside the use of evolutionary neural networks to enhance the accuracy, convergence speed, and generalization capabilities of deep mastering algorithms while managing big virtual datasets. Empirical findings imply that the proposed technique notably improves the effectiveness of deep mastering algorithms in coping with sizeable digital datasets.