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- W1936224457 abstract "This paper investigates a genetic algorithm based hyperheuristic (hyper-GA) for scheduling geographically distributed training staff and courses. The aim of the hyper-GA is to evolve a good-quality heuristic for each given instance of the problem and use this to find a solution by applying a suitable ordering from a set of low-level heuristics. Since the user only supplies a number of low-level problem-specific heuristics and an evaluation function, the hyperheuristic can easily be reimplemented for a different type of problem, and we would expect it to be robust across a wide range of problem instances. We show that the problem can be solved successfully by a hyper-GA, presenting results for four versions of the hyper-GA as well as a range of simpler heuristics and applying them to five test data set." @default.
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- W1936224457 date "2003-06-25" @default.
- W1936224457 modified "2023-09-23" @default.
- W1936224457 title "An investigation of a hyperheuristic genetic algorithm applied to a trainer scheduling problem" @default.
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- W1936224457 doi "https://doi.org/10.1109/cec.2002.1004411" @default.
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