Meta Learning for Black box Optimization
Recently, neural networks trained as optimizers under the "learning to learn" or meta learning framework have been shown to be effective for a broad range of optimization tasks including derivative free black box function optimization. Recurrent neural networks (RNNs) trained to optimize a diverse set of synthetic non convex differentiable functions via gradient descent have been effective at optimizing derivative free black box functions. In this work, we propose RNN Opt: an approach for learning RNN based optimizers for optimizing real parameter single objective continuous functions under limited budget constraints. Existing approaches utilize an observed improvement based meta learning loss function for training such models. We propose training RNN Opt by using synthetic non convex functions with known (approximate) optimal values by directly using discounted regret as our meta learning loss function. We hypothesize that a regret based loss function mimics typical testing scenarios, and would therefore lead to better optimizers compared to optimizers trained only to propose queries that improve over previous queries. Further, RNN Opt incorporates simple yet effective enhancements during training and inference procedures to deal with the following practical challenges: i) Unknown range of possible values for the black box function to be optimized, and ii) Practical and domain knowledge based constraints on the input parameters. We demonstrate the efficacy of RNN Opt in comparison to existing methods on several synthetic as well as standard benchmark black box functions along with an anonymized industrial constrained optimization problem.
ECML/PKDD