  Multiprocessors have emerged as a powerful computing means for running
realtime applications, especially where a uniprocessor system would not be
sufficient enough to execute all the tasks. The high performance and
reliability of multiprocessors have made them a powerful computing resource.
Such computing environment requires an efficient algorithm to determine when
and on which processor a given task should execute. In multiprocessor systems,
an efficient scheduling of a parallel program onto the processors that
minimizes the entire execution time is vital for achieving a high performance.
This scheduling problem is known to be NPHard. In multiprocessor scheduling
problem, a given program is to be scheduled in a given multiprocessor system
such that the programs execution time is minimized. The last job must be
completed as early as possible. Genetic algorithm (GA) is one of the widely
used techniques for constrained optimization problems. Genetic algorithms are
basically search algorithms based on the mechanics of natural selection and
natural genesis. The main goal behind research on genetic algorithms is
robustness i.e. balance between efficiency and efficacy. This paper proposes
Genetic algorithm to solve scheduling problem of multiprocessors that minimizes
the make span.
