A GA-based learning algorithm for binary neural networks
This paper presents a flexible learning algorithm for the binary neural network that can realize a desired Boolean function. The algorithm determines hidden layer parameters using a genetic algorithm. It can reduce the number of hidden neurons and can suppress parameters dispersion. These advantages are verified by basic numerical experiments.
binary neural networks genetic algorithms supervised learning separating hyperplane