自动调整学习周期，让算法自适应找到最优邻域大小。
Selection Hyper-heuristics Can Automatically Adjust the Learning Period to Optimally Solve Pseudo-Boolean Problems
- 用动态学习周期替代固定参数，实现自适应优化
- 在99%以上迭代中选择最优邻域大小，逼近理论最佳时间
- 适合需要自动化调参的组合优化研究者
最近研究表明，随机梯度超启发式算法在使用随机局部搜索（RLS）元启发式优化LeadingOnes基准问题时，能够学习到最优邻域大小。但这一能力依赖于一个特定长度的学习周期τ，不同于传统超启发式仅根据上一次迭代结果调整行为。本文提出自动设定该参数的方法，使用户无需手动控制这一新参数。我们证明，所提出的超启发式算法能在1−o(1)比例的迭代中选择最优邻域大小，因此以理论上最优（忽略低阶项）的时间完成LeadingOnes问题的求解。
原文摘要 · Abstract (English)
The Random Gradient hyper-heuristic was recently shown to be able to learn the optimal neighbourhood size when optimizing the LeadingOnes benchmark via the Randomised Local Search (RLS) meta-heuristic. However, for this to happen, a learning period of a certain length $τ$ had to be used, differently from classic hyper-heuristics, which change their behaviour based on the success of only the previous iteration. In this paper, we show how to automatically set this new parameter value, relieving the user from the non-trivial task of controlling this novel algorithm parameter. We prove that the resulting hyper-heuristic selects the optimal neighbourhood size in a $1-o(1)$ fraction of the iterations and, consequently, optimises the LeadingOnes benchmark in the best possible time (apart from lower-order terms) achievable with these neighborhood sizes.
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