An analysis of representations for hyper-heuristics for the uncapacitated examination timetabling problem in a genetic programming system

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{conf/saicsit/Pillay08,

• title = "An analysis of representations for hyper-heuristics for the uncapacitated examination timetabling problem in a genetic programming system",
• author = "Nelishia Pillay",
• bibdate = "2008-12-08",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/saicsit/saicsit2008.html#Pillay08",
• booktitle = "Proceedings of the 2008 Annual Conference of the South African Institute of Computer Scientists and Information Technologists on IT Research in Developing Countries, SAICSIT 2008",
• publisher = "ACM",
• year = "2008",
• volume = "338",
• editor = "Reinhardt A. Botha and Charmain Cilliers",
• isbn13 = "978-1-60558-286-3",
• pages = "188--192",
• series = "ACM International Conference Proceeding",
• DOI = "doi:10.1145/1456659.1456681",
• address = "Wilderness, South Africa",
• month = oct # " 6-8, 2008",
• keywords = "genetic algorithms, genetic programming",
• abstract = "Earlier research into the examination timetabling problem focused on applying different methodologies to generate solutions to the problem. More recently research has been directed at developing hyper-heuristic systems for timetable construction. Hyper-heuristic systems are used to decide which examination to schedule next during the timetable construction process and aim at allocating those examinations that are most difficult to schedule first. This study investigates using a genetic programming based hyper-heuristic system to evolve heuristic combinations for the uncapacitated examination timetabling problem. More specifically it presents and evaluates three different representations for heuristic combinations in a genetic programming system. The performance of the genetic programming based system using the different representations is applied to three examination timetabling problems with different characteristics and the performance on these problems is compared. The results obtained are also compared to that of other hyper-heuristic systems applied to the same problems.",

}

Genetic Programming entries for Nelishia Pillay

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