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.

翻译：本文研究对包含组合参数与连续参数的大规模混合黑箱函数进行优化的问题。我们证明，以往基于变异策略的进化算法虽在组合空间中具有灵活性，但在高维连续空间中，无论在理论上还是实证上均存在维度灾难问题，从而也限制了其在混合搜索空间中的应用。为解决这一难题，我们提出ES-ENAS——一种简洁模块化的联合优化框架。该方法受高效神经架构搜索（ENAS）中单次/超网络范式的启发，以高度可扩展且直观的方式，将样本高效平滑梯度技术（即进化策略）与组合优化器有机结合。通过这一设计，我们显著提升了样本效率，并在合成基准测试中进行了实证验证，同时将ES-ENAS成功应用于主流强化学习基准任务的架构搜索。