ES-ENAS：大規模ハイブリッド探索空間のための効率的進化最適化
ES-ENAS: Efficient Evolutionary Optimization for Large Hybrid Search Spaces
組合せパラメータと連続パラメータが混在する大規模ブラックボックス最適化に対し、進化戦略と組合せ最適化をスーパーネット的に統合したES-ENASを提案し、サンプル効率とRLベンチマークでのアーキテクチャ探索性能を示した。
著者: Xingyou Song, Krzysztof Choromanski, Jack Parker-Holder, Yunhao Tang, Qiuyi Zhang, Daiyi Peng, Deepali Jain, Wenbo Gao, Aldo Pacchiano, Tamas Sarlos, Yuxiang Yang
分類: cs.LG, cs.NE, cs.RO
原文アブストラクト
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.
