  Population Based Training (PBT) is an efficient hyperparameter optimization
algorithm. PBT is a single-objective algorithm, but many real-world
hyperparameter optimization problems involve two or more conflicting
objectives. In this work, we therefore introduce a multi-objective version of
PBT, MO-PBT. Our experiments on diverse multi-objective hyperparameter
optimization problems (Precision/Recall, Accuracy/Fairness,
Accuracy/Adversarial Robustness) show that MO-PBT outperforms random search,
single-objective PBT, and the state-of-the-art multi-objective hyperparameter
optimization algorithm MO-ASHA.
