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

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Author(s): Ender Özcan (University of Nottingham, UK), Mustafa Misir (Yeditepe University, Turkey), Gabriela Ochoa (University of Nottingham, UK)and Edmund K. Burke (University of Nottingham, UK)
Copyright: 2012
Pages: 22
Source title: Modeling, Analysis, and Applications in Metaheuristic Computing: Advancements and Trends
Source Author(s)/Editor(s): Peng-Yeng Yin (Ming Chuan University, Taiwan)
DOI: 10.4018/978-1-4666-0270-0.ch003

Keywords: Algorithms / Artificial Intelligence / Computer Science & IT / Information Science Reference

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Abstract

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.

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

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