  The no free lunch theorem states that no model is better suited to every
problem. A question that arises from this is how to design methods that propose
optimizers tailored to specific problems achieving state-of-the-art
performance. This paper addresses this issue by proposing the use of
meta-learning to infer population-based black-box optimizers that can
automatically adapt to specific classes of problems. We suggest a general
modeling of population-based algorithms that result in Learning-to-Optimize
POMDP (LTO-POMDP), a meta-learning framework based on a specific partially
observable Markov decision process (POMDP). From that framework's formulation,
we propose to parameterize the algorithm using deep recurrent neural networks
and use a meta-loss function based on stochastic algorithms' performance to
train efficient data-driven optimizers over several related optimization tasks.
The learned optimizers' performance based on this implementation is assessed on
various black-box optimization tasks and hyperparameter tuning of machine
learning models. Our results revealed that the meta-loss function encourages a
learned algorithm to alter its search behavior so that it can easily fit into a
new context. Thus, it allows better generalization and higher sample efficiency
than state-of-the-art generic optimization algorithms, such as the Covariance
matrix adaptation evolution strategy (CMA-ES).
