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O'Brien, Ross (2008)
Languages: English
Types: Unknown
For decades, optimisation research has investigated methods to find optimal solutions to many problems in the fields of scheduling, timetabling and rostering. A family of abstract methods known as metaheuristics have been developed and applied to many of these problems, but their application to specific problems requires problem-specific coding and parameter adjusting to produce the best results for that problem. Such specialisation makes code difficult to adapt to new problem instances or new problems. One methodology that intended to increase the generality of state of the art algorithms is known as hyperheuristics. Hyperheuristics are algorithms which construct algorithms: using "building block" heuristics, the higher-level algorithm chooses between heuristics to move around the solution space, learning how to use the heuristics to find better solutions. We introduce a new hyperheuristic based upon the well-known ant algorithm metaheuristic, and apply it towards several real-world problems without parameter tuning, producing results that are competitive with other hyperheuristic methods and established bespoke metaheuristic techniques.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • of the 2007 IEEE Symposium on Computational Intelligence in Scheduling (CISched2007), Hilton Hawaiian Village, Honolulu, Hawaii, USA, 1-5 April 2007.
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    • of the 2007 Genetic and Evolutionary Computation Conference (GECCO '07), London, UK, July 7-11 2007. ACM 2007.
    • E. K. Burke, M. R. Hyde, G. Kendall, and J. R. Woodward. The scalability of evolved online bin packing heuristics. In Proceedings of the Congress on Evolutionary Computation (CEC 2007), Swissotel The Stamford, Singapore, September 25-28 2007.
    • E. Burke, K. Jackson, J.H. Kingston, and R. Weare. Automated university timetabling: The state of the art. The Computer Journal, 40(9):565{571, 1997.
    • E. Burke, G. Kendall, and E. Soubeiga. A tabu-search hyperheuristic for timetabling and rostering. Journal of Heuristics, 9(6):451{470, 2003.
    • K. Chakhlevitch and P. Cowling. Choosing the ttest subset of low level heuristics in a hyperheuristic framework. In Proceedings of the Fifth European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP2005), Lausanne, Switzerland, 2005.
    • A.T. Ernst, H. Jiand, M. Krishnamoorthy, and D. Sier. Sta scheduling and rostering: a review of applications, methods and models. European Journal of Operational Research, 2001.
    • H. Fisher and G. L. Thompson. Probabilitistic learning combinations of local job-shop scheduling rules. In Factory Scheduling Conference, Carnegie Institute of Technology, May 10-12 1961.
    • L. Han and G. Kendall. Guided operators for a hyper-heuristic genetic algorithm. In T. D. Gedeon and L. C. C. Fung, editors, Proceedings of AI2003: Advances in Arti cial Intelligence. The 16th Australian Conference S. Kirkpatrick, C.D. Gelatt, and M.P. Vecchi. Optimization by simulated annealing, 1983.
    • Daduna, I. Branco, and J.M.P. Paixao, editors, Computer Aided Transirt Scheduling, pages 173{187. Springer-Verlag, 1995.
  • No related research data.
  • Discovered through pilot similarity algorithms. Send us your feedback.

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