基于种群演化的超参数异步并行搜索  

Asynchronous parallel hyperparameter search with population evolution

作　　者：蒋云良[1,2] 赵康 曹军杰 范婧 刘勇[4] JIANG Yun-liang;ZHAO Kang;CAO Jun-jie;FAN Jing;LIU Yong(School of Information Engineering,HuzhouUniversity,Huzhou 313000,China;Zhejiang ProvinceKey Laboratory of Smart Management&Application of Modern Agricultural Resources,Huzhou University,Huzhou 313000,China;College of Mathematics and Computer Science,Zhejiang Normal University,Jinhua 321004,China;Institute of Cyber Systems and Control,Zhejiang University,Hangzhou 310027,China)

机构地区：[1]湖州师范学院信息工程学院,浙江湖州313000 [2]湖州师范学院浙江省现代农业资源智慧管理与应用研究重点实验室,浙江湖州313000 [3]浙江师范大学数学与计算机科学学院,浙江金华321004 [4]浙江大学智能系统与控制研究所,杭州310027

出　　处：《控制与决策》2021年第8期1825-1833,共9页Control and Decision

基　　金：国家自然科学基金项目(61771193);浙江省重点研发计划项目(2020C01097,2020C02020)。

摘　　要：近年来随着深度学习尤其是深度强化学习模型的不断增大,其训练成本即超参数的搜索空间也在不断变大,然而传统超参数搜索算法大部分是基于顺序执行训练,往往需要等待数周甚至数月才有可能找到较优的超参数配置.为解决深度强化学习超参数搜索时间长和难以找到较优超参数配置问题,提出一种新的超参数搜索算法—–基于种群演化的超参数异步并行搜索(PEHS).算法结合演化算法思想,利用固定资源预算异步并行搜索种群模型及其超参数,从而提高算法性能.设计实现在Ray并行分布式框架上运行的参数搜索算法,通过实验表明在并行框架上基于种群演化的超参数异步并行搜索的效果优于传统超参数搜索算法,且性能稳定.In recent years, with the continuous increase of deep learning models, especially deep reinforcement learning models, the training cost, that is, the search space of hyperparameters, has also continuously increased. However, most traditional hyperparameter search algorithms are based on sequential execution of training, which often takes weeks or even months to find a better hyperparameter configuration. In order to solve the problem of the long search time hyperparameters and the difficulty in finding a better hyperparameter of deep reinforcement learning configuration, this paper proposes a new hyper-parameter search algorithm, named asynchronous parallel hyperparameter search with population evolution.This algorithm combines the idea of evolutionary algorithms and uses a fixed resource budget to search the population model and its hyperparameters asynchronously and in parallel, thereby improving the performance of the algorithm. It is realized that a parameter search algorithm can run on the Ray parallel distributed framework. Experiments show that the parametric asynchronous parallel search based on population evolution on the parallel framework is better than the traditional hyperparameter search algorithm, and its performance is stable.

关 键 词：超参数搜索 种群 演化算法 异步并行 深度学习 并行框架 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]