Stochastic Schemata Exploiter-Based Optimization of Convolutional Neural Network

Stochastic Schemata Exploiter (SSE), which is one of Evolutionary Computations, is designed to find the optimal solution of the function. When comparing it with Genetic Algorithm (GA), which is a population evolutionary computation, SSE has interesting features; quick convergence and smaller number of control parameters. In this study, SSE is applied for designing hyperparameters and structure of Convolutional Neural Network (CNN). The validity of the proposal algorithm is discussed for determining CNN in experiments using CIFAR-10. The results show that SSE can find the better parameters and structure of CNN than GA.