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 up to $K$ 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. 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 eight-line sorting network design (a single-population optimization task with limited evaluation-time variability), achieving an over two-fold speedup. Next, it was evaluated in 11-bit multiplexer design (a single-population discovery task with extended variability), where a 14-fold speedup was observed. It was then scaled up to ENAS for image captioning (a multi-population open-ended-optimization task), resulting in an over two-fold speedup. In all 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.

翻译：许多进化算法（EA）利用了候选解的并行评估优势。然而，当评估时间差异显著时，大量工作节点（即计算客户端）会因等待下一代生成而长期处于空闲状态。进化神经架构搜索（ENAS）作为一类优化深度神经网络架构与超参数的EA，尤其容易受此问题影响。本文提出一种通用异步评估策略（AES），并将其适配至ENAS场景。AES通过维护一个包含最多$K$个候选个体的队列，当其中$M<<K$个个体完成评估后即进入下一代，从而提升系统吞吐量。通过实验确定$M$的适宜值，以平衡多样性与效率。为展示AES的普适性与效力，首先在八线排序网络设计（评估时间差异有限的单种群优化任务）中验证，获得两倍以上的加速效果；其次在11位多路复用器设计（评估时间差异显著的单种群发现任务）中测试，实现14倍加速；最后将其扩展至图像描述生成的ENAS（多种群开放式优化任务），仍取得两倍以上的加速。在所有问题中均观察到多倍的性能提升，表明AES是并行化具有长时且可变评估时间复杂系统（如ENAS）的一种有效方法。