  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. Children
are produced from three parents; two contributing hyperparameters and one
contributing the parameters. Our version of population-based training (PBT)
combines traditional gradient-based approaches such as stochastic gradient
descent (SGD) with our GA to optimize both parameters and hyperparameters
across SGD epochs. Our improvements over traditional PBT provide an increased
speed of adaptation and a greater ability to shed deleterious genes from the
population. Our methods improve final accuracy as well as time to fixed
accuracy on a wide range of deep neural network architectures including
convolutional neural networks, recurrent neural networks, dense neural
networks, and capsule networks.
