Schedule length optimization by elite-genetic algorithm using rank based selection for multiprocessor systems

Scheduling tasks in a multiprocessor system is found to be a NP-hard problem and a considerable amount of time is used up when it is solved using conventional techniques. Therefore, evolutionary algorithms like Genetic Algorithms (GA) have been explored for scheduling tasks in a multiprocessor system. GA can be implemented in various manners. This paper investigates the performance of GA with two different selection operators. This paper also studies how introducing elitism effects the performance of GA. Extensive simulations have been carried out in order to find the better candidate among the two selection operators. The decision is made depending on stability of the GA output, the rate of convergence of output and the ability of GA to give an output which is as close as possible to the actual output.