Name
Abstract | ||
|---|---|---|
Hyper-heuristic is a class of methodologies which automates the process of selecting or generating a set of heuristics to solve various optimization problems. A traditional hyper-heuristic model achieves this through a high-level heuristic that consists of two key components, namely a heuristic selection method and a move acceptance method. The effectiveness of the high-level heuristic is highly problem dependent due to the landscape properties of different problems. Most of the current hyper-heuristic models formulate a high-level heuristic by matching different combinations of components manually. This article proposes a method to automatically design the high-level heuristic of a hyper-heuristic model by utilizing a reinforcement learning technique. More specifically, Q-learning is applied to guide the hyper-heuristic model in selecting the proper components during different stages of the optimization process. The proposed method is evaluated comprehensively using benchmark instances from six problem domains in the Hyper-heuristic Flexible Framework. The experimental results show that the proposed method is comparable with most of the top-performing hyper-heuristic models in the current literature. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.ins.2018.01.005 | Information Sciences |
Keywords | Field | DocType |
Hyper-heuristic,Q-learning,Automatic design,Cross-domain heuristic search | Heuristic,Hyper-heuristic,Heuristics,Artificial intelligence,Optimization problem,Mathematics,Machine learning,Reinforcement learning | Journal |
Volume | ISSN | Citations |
436 | 0020-0255 | 3 |
PageRank | References | Authors |
0.39 | 34 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shin Siang Choong | 1 | 14 | 2.20 |
| Li-Pei Wong | 2 | 109 | 8.32 |
| Chee Peng Lim | 3 | 1459 | 122.04 |