Pietro S. Oliveto
2 indexed papers
Recent (6 mo)
2With code
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226
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Neural Computing×2AI×1Algorithms×1Optimization and Control×1
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2026
Selection Hyper-heuristics Can Automatically Adjust the Learning Period to Optimally Solve Pseudo-Boolean Problems
This paper introduces a method to automatically determine the optimal learning period ($ au$) for the Random Gradient hyper-heuristic, enabling it to optimally solve Pseudo-Boolean Problems without manual parameter tuning.
On the Runtime Analysis of Reinforcement Learning Hyper-Heuristics
This paper rigorously proves that a Reinforcement Learning Hyper-heuristic (RLHH) optimizes the LeadingOnes benchmark function with optimal expected runtime using two random local search operators, outperforming the Generalised Random Gradient HH.
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Papers
cs.NEEmpiricalRecentJul 24, 2026
On the Runtime Analysis of Reinforcement Learning Hyper-Heuristics
This paper rigorously proves that a Reinforcement Learning Hyper-heuristic (RLHH) optimizes the LeadingOnes benchmark function with optimal expected runtime using two random local search operators, ou…
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