Reinforcement Learning for Cross-Domain Hyper-Heuristics
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.
Cite
Text
Mischek and Musliu. "Reinforcement Learning for Cross-Domain Hyper-Heuristics." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/664Markdown
[Mischek and Musliu. "Reinforcement Learning for Cross-Domain Hyper-Heuristics." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/mischek2022ijcai-reinforcement/) doi:10.24963/IJCAI.2022/664BibTeX
@inproceedings{mischek2022ijcai-reinforcement,
title = {{Reinforcement Learning for Cross-Domain Hyper-Heuristics}},
author = {Mischek, Florian and Musliu, Nysret},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2022},
pages = {4793-4799},
doi = {10.24963/IJCAI.2022/664},
url = {https://mlanthology.org/ijcai/2022/mischek2022ijcai-reinforcement/}
}