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- W4285602012 abstract "In this paper, we propose a new hyper-heuristic approach that uses reinforcement learning to automatically learn the selection of low-level heuristics across a wide range of problem domains. We provide a detailed analysis and evaluation of the algorithm components, including different ways to represent the hyper-heuristic state space and reset strategies to avoid unpromising areas of the solution space. Our methods have been evaluated using HyFlex, a well-known benchmarking framework for cross-domain hyper-heuristics, and compared with state-of-the-art approaches. The experimental evaluation shows that our reinforcement-learning based approach produces results that are competitive with the state-of-the-art, including the top participants of the Cross Domain Hyper-heuristic Search Competition 2011." @default.
- W4285602012 created "2022-07-16" @default.
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- W4285602012 date "2022-07-01" @default.
- W4285602012 modified "2023-10-14" @default.
- W4285602012 title "Reinforcement Learning for Cross-Domain Hyper-Heuristics" @default.
- W4285602012 doi "https://doi.org/10.24963/ijcai.2022/664" @default.
- W4285602012 hasPublicationYear "2022" @default.
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