Abstract
In this paper genetic algorithms (GA) are used to evolve cellular automata (CA) structures suitable to perform scheduling tasks of a parallel program in two-processor systems. For this purpose a program graph is considered as CA with elementary automata changing their states according to local rules. Changing states of CA can be interpreted in terms of migration of tasks in a multiprocessor system. To design rules of CA two neighborhoods are considered and studied. There are two phases of the proposed CA-based scheduling algorithm. In the first phase effective rules for CA are discovered by GA. In the second phase CA works as a distributed scheduler. In this phase, for any initial allocation of tasks in a system, CA-based scheduler is able to find an allocation which minimizes the total execution time of the program in the system.
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© 1998 Springer-Verlag Berlin Heidelberg
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Seredyński, F. (1998). Discovery with genetic algorithm scheduling strategies for cellular automata. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN V. PPSN 1998. Lecture Notes in Computer Science, vol 1498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056906
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DOI: https://doi.org/10.1007/BFb0056906
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