An investigation of a hyperheuristic genetic algorithm applied to a trainer scheduling problem

Authors:
P. Cowling;G. Kendall;Limin Han
Affiliations:
Dept. of Comput., Bradford Univ., UK;Dept. of Electr. Eng., Sharif Univ. of Technol., Tehran, Iran;LIFL, Lille Univ., Villeneuve d'Ascq, France
Venue:
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Year:
2002

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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.