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• W1551001158 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." @default.
• W1551001158 created "2016-06-24" @default.
• W1551001158 creator A5070823139 @default.
• W1551001158 creator A5074319022 @default.
• W1551001158 date "2003-01-01" @default.
• W1551001158 modified "2023-10-12" @default.
• W1551001158 title "Guided Operators for a Hyper-Heuristic Genetic Algorithm" @default.
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• W1551001158 doi "https://doi.org/10.1007/978-3-540-24581-0_69" @default.
• W1551001158 hasPublicationYear "2003" @default.
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