Fast and Efficient Local Search for Genetic Programming Based Loss Function Learning

The author proposes a new framework, EvoMAL, for meta-learning symbolic loss functions using a hybrid neuro-symbolic search approach. This approach combines genetic programming with unrolled differentiation to optimize symbolic loss functions efficiently.