Multiprocessor scheduling using a problem-space genetic algorithm

In this paper, we present a technique based on the problem-space genetic algorithm (PSGA) for the static scheduling of directed acyclic graphs onto homogeneous multiprocessor systems to reduce the response-time. The PSGA based approach combines genetic algorithms, with a list scheduling heuristic to search a large solution space efficiently and effectively. Comparison of results with the genetic algorithm based scheduling technique for the Stanford manipulator and the Elbow manipulator examples shows a significant improvement in the response-time.