Evolutionary multiprocessor task scheduling

The genetic algorithm has, to date, been applied to a wide range of problems. It is an ideal tool to solve problem in need of multiple, often interdependent requirements. This is because it has the ability to search within a large solution space while at the same time meeting criteria and constraints within the problem's boundaries. In this paper, we apply this heuristic to the problem of multiprocessor task scheduling - assigning a group of predefined tasks to a set of predefined processors. This task execution should take a minimum amount of time while taking into account certain constraints - e.g., prerequisite constraints between the tasks. Aside from using the genetic algorithm, we incorporate a local search method called a memetic within the genetic algorithm as a global search. Since the tasks are operating in a multiprocessor environment, we also attempt to reduce processor temperature by reducing the total power consumption and load balancing amongst the processors.