Many evolutionary algorithms (EAs) take advantage of parallel evaluation of
candidates. However, if evaluation times vary significantly, many worker nodes
(i.e.,\ compute clients) are idle much of the time, waiting for the next
generation to be created. Evolutionary neural architecture search (ENAS), a
class of EAs that optimizes the architecture and hyperparameters of deep neural
networks, is particularly vulnerable to this issue. This paper proposes a
generic asynchronous evaluation strategy (AES) that is then adapted to work
with ENAS. AES increases throughput by maintaining a queue of upto K
individuals ready to be sent to the workers for evaluation and proceeding to
the next generation as soon as M<<K individuals have been evaluated by the
workers. A suitable value for M is determined experimentally, balancing
diversity and efficiency. To showcase the generality and power of AES, it was
first evaluated in 11-bit multiplexer design (a single-population verifiable
discovery task) and then scaled up to ENAS for image captioning (a
multi-population open-ended-optimization task). In both problems, a multifold
performance improvement was observed, suggesting that AES is a promising method
for parallelizing the evolution of complex systems with long and variable
evaluation times, such as those in ENAS