Representation and Genetic Operators in Tasks Matching & Scheduling by Genetic Algorithms
任务分配与调度中的遗传算法：知识表示与遗传算法子研究

Task matching and scheduling play an important role in parallel and distributed systems.In order to use genetic algorithms(Gas) for tasks matching and scheduling,not only appropriate representations of solutions but also genetic operators’efficiency and generality are very important.In this paper,analysis between problem space and representation space is given at the first.Then based on the representation of permutation,two general efficient genetic operators are proposed,order crossover(OCX)and migration. OCX generates new schedules with heuristic due to the problem space with constraints among tasks. Migration transfers a task from one processor to another within a schedule. The simulation results of algorithms and conclusions are given at last.