Hybrid genetic algorithms for scheduling partially ordered tasks in a multi-processor environment

Scheduling partially ordered tasks in a multiple-processor environment is a very complex combinatorial optimization problem. In this paper, hybrid genetic algorithms for the scheduling optimization problem are presented. We first present a non-string representation of the solutions for scheduling problems. Then we provide a hybrid mechanism for the choice of genetic operators. The issue of illegal solution is addressed as well. Experimental results for the choice of parameters and the comparison of GA and Tabu search are also presented