no code implementations • 12 Jan 2019 • Aaron Vose, Jacob Balma, Alex Heye, Alessandro Rigazzi, Charles Siegel, Diana Moise, Benjamin Robbins, Rangan Sukumar
We propose a genetic algorithm (GA) for hyperparameter optimization of artificial neural networks which includes chromosomal crossover as well as a decoupling of parameters (i. e., weights and biases) from hyperparameters (e. g., learning rate, weight decay, and dropout) during sexual reproduction.