Selection Hyper-heuristics Can Automatically Adjust the Learning Period to Optimally Solve Pseudo-Boolean Problems

arXiv:2605.29916 · cs.NE, cs.AI, cs.DS, math.OC · Submitted 2026-05-28 · Read on arXiv

cs.NE, cs.AI, cs.DS, math.OC

Submitted: 2026-05-28

Updated: 2026-08-26

Comments: Extends significantly our paper at GECCO 2018

Journal ref: Artificial Intelligence 357:104560 (2026)

DOI: 10.1016/j.artint.2026.104560

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Adaptation — 12× in this paper · explained in 1 episode
• Adaptive Random Gradient — 10× in this paper
• Benchmark — 10× in this paper · explained in 2 episodes
• Randomised Local Search — 5× in this paper
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Generalised Random Gradient — 2× in this paper
• Adaptive Algorithm — 1× in this paper · explained in 1 episode
• Combinatorial Optimization — 1× in this paper · explained in 2 episodes
• Hamming Distance — 1× in this paper · explained in 2 episodes
• Multi-objective Optimization — 1× in this paper · explained in 4 episodes

Sources

• Hyper-Heuristics Can Profit From Global Variation Operators

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