  In multiprocessor systems, one of the main factors of systems' performance is
task scheduling. The well the task be distributed among the processors the well
be the performance. Again finding the optimal solution of scheduling the tasks
into the processors is NP-complete, that is, it will take a lot of time to find
the optimal solution. Many evolutionary algorithms (e.g. Genetic Algorithm,
Simulated annealing) are used to reach the near optimal solution in linear
time. In this paper we propose a heuristic for genetic algorithm based task
scheduling in multiprocessor systems by choosing the eligible processor on
educated guess. From comparison it is found that this new heuristic based GA
takes less computation time to reach the suboptimal solution.
