Evolutionary Training of Binary Neural Networks by Genetic Algorithm - Okada, Hidehiko (International Journal of Scientific Research in Computer Science and Engineering, 2021)
A problem with deep neural networks is that the memory size for recording a trained model becomes large. A solution to this problem is to make the parameter values binary. A challenge for the binary neural networks is that they cannot be trained by the ordinary gradient-based optimization methods. The author previously applied Evolution Strategy (ES) to the training of binary neural networks and evaluates its ability. This paper applies Genetic Algorithm (GA), another instance of evolutionary algorithms, and compares GA with ES. The experimental results with a classification task revealed that GA could also optimize parameter values well so that the trained model accurately classified both trained and untrained data. No significant difference was observed between classification accuracies by GA and ES.