  As the complexity of neural network models has grown, it has become
increasingly important to optimize their design automatically through
metalearning. Methods for discovering hyperparameters, topologies, and learning
rate schedules have lead to significant increases in performance. This paper
shows that loss functions can be optimized with metalearning as well, and
result in similar improvements. The method, Genetic Loss-function Optimization
(GLO), discovers loss functions de novo, and optimizes them for a target task.
Leveraging techniques from genetic programming, GLO builds loss functions
hierarchically from a set of operators and leaf nodes. These functions are
repeatedly recombined and mutated to find an optimal structure, and then a
covariance-matrix adaptation evolutionary strategy (CMA-ES) is used to find
optimal coefficients. Networks trained with GLO loss functions are found to
outperform the standard cross-entropy loss on standard image classification
tasks. Training with these new loss functions requires fewer steps, results in
lower test error, and allows for smaller datasets to be used. Loss-function
optimization thus provides a new dimension of metalearning, and constitutes an
important step towards AutoML.
