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cs.NE2026
On the Runtime Analysis of Reinforcement Learning Hyper-Heuristics
Pietro S. Oliveto, Zhenyu Wang, Peizhou Wu +1
Selection Hyper-heuristics (HHs) automate algorithmic design by selecting from a set of low-level heuristics which one to apply at each stage of the optimisation process. Several i…
cs.NE2026★ 2 cited
Selection Hyper-heuristics Can Automatically Adjust the Learning Period to Optimally Solve Pseudo-Boolean Problems
Benjamin Doerr, Pietro S. Oliveto, John Alasdair Warwicker
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…