Comparative Study of Genetic Operators and Parameters for Multiprocessor Task Scheduling
Task scheduling in multiprocessor systems is one of the main factors of systems performance. In this paper, the problem of scheduling of tasks in multiprocessor system is described as finding optimal sequence of the task (called schedule) such that make span can be minimized. Finding the optimal solution of scheduling the tasks into the processors is NP-complete. Genetic Algorithm (GA) has been developed as a powerful tool for solving constrained optimization problems. This paper presented the results of experimental comparison of six different combinations of crossover (i.e. PMX, OX and CX) and mutation (i.e. insertion and swap) operators and also analyzed the effect of varying the genetic control parameters on objective function for considered scheduling problem.