  Meta-learning models, or models that learn to learn, have been a long-desired
target for their ability to quickly solve new tasks. Traditional meta-learning
methods can require expensive inner and outer loops, thus there is demand for
algorithms that discover strong learners without explicitly searching for them.
We draw parallels to the study of evolvable genomes in evolutionary systems --
genomes with a strong capacity to adapt -- and propose that meta-learning and
adaptive evolvability optimize for the same objective: high performance after a
set of learning iterations. We argue that population-based evolutionary systems
with non-static fitness landscapes naturally bias towards high-evolvability
genomes, and therefore optimize for populations with strong learning ability.
We demonstrate this claim with a simple evolutionary algorithm,
Population-Based Meta Learning (PBML), that consistently discovers genomes
which display higher rates of improvement over generations, and can rapidly
adapt to solve sparse fitness and robotic control tasks.
