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Guided Operators for a Hyper-Heuristic Genetic Algorithm

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AI 2003: Advances in Artificial Intelligence (AI 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2903))

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Abstract

We have recently introduced a hyper-heuristic genetic algorithm (hyper-GA) with an adaptive length chromosome which aims to evolve an ordering of low-level heuristics so as to find good quality solutions to given problems. The guided mutation and crossover hyper-GA, the focus of this paper, extends that work. The aim of a guided hyper-GA is to make the dynamic removal and insertion of heuristics more efficient, and evolve sequences of heuristics in order to produce promising solutions more effectively. We apply the algorithm to a geographically distributed training staff and course scheduling problem to compare the computational result with the application of other hyper-GAs. In order to show the robustness of hyper-GAs, we apply our methods to a student project presentation scheduling problem in a UK university and compare results with the application of another hyper-heuristic method.

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Author information

Authors and Affiliations

  1. Automated Scheduling, Optimisation and Planning Research Group, School of Computer Science and IT University of Nottingham, NG8 1BB, UK

    Limin Han & Graham Kendall

Authors
  1. Limin Han
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  2. Graham Kendall
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Australian National University, ACT 0200, Acton, Australia

    Tamás (Tom) Domonkos Gedeon

  2. Murdoch University,  

    Lance Chun Che Fung

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© 2003 Springer-Verlag Berlin Heidelberg

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Han, L., Kendall, G. (2003). Guided Operators for a Hyper-Heuristic Genetic Algorithm. In: Gedeon, T.(.D., Fung, L.C.C. (eds) AI 2003: Advances in Artificial Intelligence. AI 2003. Lecture Notes in Computer Science(), vol 2903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24581-0_69

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  • DOI: https://doi.org/10.1007/978-3-540-24581-0_69

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20646-0

  • Online ISBN: 978-3-540-24581-0

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