Meta Learning Black-Box Population-Based Optimizers

Understand

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).