计算机科学
启发式
遗传程序设计
工作流程
调度(生产过程)
一般化
动态规划
采样(信号处理)
遗传算法
数学优化
机器学习
数学
算法
数据库
数学分析
滤波器(信号处理)
计算机视觉
操作系统
作者
Yifan Yang,Gang Chen,Hui Ma,Sven Hartmann,Mengjie Zhang
标识
DOI:10.1016/j.ins.2024.120975
摘要
Genetic Programming Hyper-heuristics (GPHH) have been successfully used to evolve scheduling rules for Dynamic Workflow Scheduling (DWS) as well as other challenging combinatorial optimization problems. The method of sampling training instances has a significant impact on the generalization ability of GPHH, yet they are rarely addressed in existing research. This article aims to fill this gap by proposing a GPHH algorithm with a sampling strategy to thoroughly investigate the impact of six instance sampling strategies on algorithmic generalization, including one rotation strategy, three mini-batch strategies, and two hybrid strategies. Experiments across four scenarios with varying settings reveal that: (1) mini-batch with random sampling can outperform rotation in generalizing to unseen workflow scheduling problems under the same computational cost; (2) employing a hybrid strategy that combines rotation and mini-batch further enhances the generalization ability of GPHH; and (3) mini-batch and hybrid strategies can effectively enable heuristics trained on small-scale training instances generalizing well to large-scale unseen ones. These findings highlight the potential of mini-batch strategies in GPHH, offering improved generalization performance while maintaining diversity and suggesting promising avenues for further exploration in GPHH domains.
科研通智能强力驱动
Strongly Powered by AbleSci AI