Genetic list scheduling algorithm for scheduling and allocation on a loosely coupled heterogeneous multiprocessor system

Our problem consists of a partially ordered set of tasks communicating over a shared bus which are to be mapped to a heterogeneous multiprocessor system. The goal is to minimize the makespan, while satisfying constraints implied by data dependencies and exclusive resource usage. We present a new efficient heuristic approach based on list scheduling and genetic algorithms, which finds the optimum in few seconds on average even for large examples (up to 96 tasks). The superiority of our algorithm compared to some other algorithms is demonstrated