Genetic Fuzzy Systems applied to Online Job Scheduling

This paper presents a comparison of three different design concepts for genetic fuzzy systems. We apply a symbiotic evolution that uses the Michigan approach and two approaches that are based on the Pittsburgh approach: a complete optimization of the problem and a cooperative coevolutionary algorithm. The three different genetic fuzzy systems are applied to a real-world online problem, the generation of scheduling strategies for massively parallel processing systems. The genetic fuzzy systems must classify different scheduling states and decide about a corresponding scheduling strategy within each scheduling state. The main challenge arise in the delayed reward given by a critic. Therefore, it is impossible to directly evaluate the assignment of scheduling strategies to scheduling states. In our paper, the three design concepts are evaluated with real workload traces considering result quality, computational effort, convergence behavior, and robustness.

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