Abstract
In this article, we present a method of improving the genetic algorithms in the task scheduling of grid environment due to the dynamic variability characteristic of grid. First, we review the crossover probability P c and mutation probability P m, the key parameters affecting the performance of genetic algorithm. Next, using the adaptive thinking and population fitness which represents the performance of grid resource scheduling, we present an adaptive genetic algorithm, giving a reasonable way to select crossover probability and mutation probability. It helps P c and P m can be adjusted automatically with the change of the population fitness; therefore we can get a good resource scheduling. Finally, we describe the results of the test, showing that the improved adaptive genetic algorithms can make the grid resource scheduling have good population fitness.
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Yuan, Jb., Luo, Jm., Duan, Bj. (2010). Adaptive Control in Grid Computing Resource Scheduling. In: Zhang, W., Chen, Z., Douglas, C.C., Tong, W. (eds) High Performance Computing and Applications. Lecture Notes in Computer Science, vol 5938. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11842-5_74
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DOI: https://doi.org/10.1007/978-3-642-11842-5_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11841-8
Online ISBN: 978-3-642-11842-5
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