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Scheduling multiprocessor tasks with genetic algorithms

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3 Author(s)
R. C. Correa ; Dept. de Comput., Univ. Fed. do Ceara, Brazil ; A. Ferreira ; P. Rebreyend

In the multiprocessor scheduling problem, a given program is to be scheduled in a given multiprocessor system such that the program's execution time is minimized. This problem being very hard to solve exactly, many heuristic methods for finding a suboptimal schedule exist. We propose a new combined approach, where a genetic algorithm is improved with the introduction of some knowledge about the scheduling problem represented by the use of a list heuristic in the crossover and mutation genetic operations. This knowledge-augmented genetic approach is empirically compared with a “pure” genetic algorithm and with a “pure” list heuristic, both from the literature. Results of the experiments carried out with synthetic instances of the scheduling problem show that our knowledge-augmented algorithm produces much better results in terms of quality of solutions, although being slower in terms of execution time

Published in:

IEEE Transactions on Parallel and Distributed Systems  (Volume:10 ,  Issue: 8 )