  In this paper, we approach the problem of optimizing blackbox functions over
large hybrid search spaces consisting of both combinatorial and continuous
parameters. We demonstrate that previous evolutionary algorithms which rely on
mutation-based approaches, while flexible over combinatorial spaces, suffer
from a curse of dimensionality in high dimensional continuous spaces both
theoretically and empirically, which thus limits their scope over hybrid search
spaces as well. In order to combat this curse, we propose ES-ENAS, a simple and
modular joint optimization procedure combining the class of sample-efficient
smoothed gradient techniques, commonly known as Evolutionary Strategies (ES),
with combinatorial optimizers in a highly scalable and intuitive way, inspired
by the one-shot or supernet paradigm introduced in Efficient Neural
Architecture Search (ENAS). By doing so, we achieve significantly more sample
efficiency, which we empirically demonstrate over synthetic benchmarks, and are
further able to apply ES-ENAS for architecture search over popular RL
benchmarks.
