A Reinforcement Learning - Great-Deluge Hyper-Heuristic for Examination Timetabling

Journal Article

Hyper-heuristics can be identified as methodologies that search the space generated by a finite set of low level heuristics for solving search problems. An iterative hyper-heuristic framework can be thought of as requiring a single candidate solution and multiple perturbation low level heuristics. An initially generated complete solution goes through two successive processes (heuristic selection and move acceptance) until a set of termination criteria is satisfied. A motivating goal of hyper-heuristic research is to create automated techniques that are applicable to a wide range of problems with different characteristics. Some previous studies show that different combinations of heuristic selection and move acceptance as hyper-heuristic components might yield different performances. This study investigates whether learning heuristic selection can improve the performance of a great deluge based hyper-heuristic using an examination timetabling problem as a case study.

Full Text

Duke Authors

Cited Authors

  • Özcan, E; Misir, M; Ochoa, G; Burke, EK

Published Date

  • January 1, 2010

Published In

Volume / Issue

  • 1 / 1

Start / End Page

  • 39 - 59

Published By

Electronic International Standard Serial Number (EISSN)

  • 1947-8291

International Standard Serial Number (ISSN)

  • 1947-8283

Digital Object Identifier (DOI)

  • 10.4018/jamc.2010102603


  • ng