2 citations · 3 across the 2 of their papers we have counts for
2 papers
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…
cs.NE2018★ 1 cited
Simple Hyper-heuristics Control the Neighbourhood Size of Randomised Local Search Optimally for LeadingOnes
Andrei Lissovoi, Pietro S. Oliveto, John Alasdair Warwicker
Selection HHs are randomised search methodologies which choose and execute heuristics during the optimisation process from a set of low-level heuristics. A machine learning mechani…