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Non Linear Great Deluge Hyper Heuristic with Reinforcement Learning for Scheduling Problem

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dc.contributor.author Sin, Ei Shwe
dc.date.accessioned 2019-07-25T04:42:25Z
dc.date.available 2019-07-25T04:42:25Z
dc.date.issued 2010-12-16
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/1270
dc.description.abstract Nowadays, scheduling problems arise in almost all areas of human activity. To handle the complexity of the real world scheduling problems, many researchers have been invested over the years. Currently research is being directed to raise the level of generality. Therefore, this has led to the development of hyper heuristics system. A hyper heuristic is high level problem solving methodology that performs a search over the space generated by a set of low level heuristics. A motivating goal of hyper heuristic research is to create automated techniques that applicable to a wide range of problems with different characteristics. One of the hyper heuristic frameworks is based on a single point search containing two main stages: heuristic selection and move acceptance. By using exam timetabling problem as a test bed, this paper proposes the non linear great deluge hyper heuristic with reinforcement learning method to intend to improve the performance of hyper heuristic en_US
dc.language.iso en en_US
dc.publisher Fifth Local Conference on Parallel and Soft Computing en_US
dc.title Non Linear Great Deluge Hyper Heuristic with Reinforcement Learning for Scheduling Problem en_US
dc.type Article en_US


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