Optimizing method for Neural Network based on Genetic Random Weight Change Learning Algorithm
This work addresses optimization challenges in neural networks for researchers, but it appears incremental as it builds on existing methods.
The authors tackled the problem of optimizing neural networks by proposing a hybrid Genetic Random Weight Change (GRWC) algorithm, which combines Random Weight Change and Genetic Algorithms, and demonstrated its performance on the MNIST dataset.
Random weight change (RWC) algorithm is extremely component and robust for the hardware implementation of neural networks. RWC and Genetic algorithm (GA) are well known methodologies used for optimizing and learning the neural network (NN). Individually, each of these two algorithms has its strength and weakness along with separate objectives. However, recently, researchers combine these two algorithms for better learning and optimization of NN. In this paper, we proposed a methodology by combining the RWC and GA, namely Genetic Random Weight Change (GRWC), as well as demonstrate a seminal way to reduce the complexity of the neural network by removing weak weights of GRWC. In contrast to RWC and GA, GRWC contains an effective optimization procedure which is worthy at exploring a large and complex space in intellectual strategies influenced by the GA/RWC synergy. The learning behavior of the proposed algorithm was tested on MNIST dataset and it was able to prove its performance.