TASK MATCHING AND SCHEDULING BY USING GENETIC ALGORITHMS

Task matching and scheduling by using genetic algorithm based approaches have been the attractive problems, while the construction of initial population and genetic operators in most of the previous work have some limitations. In this paper, an improved algorithm is proposed based on the integration of genetic algorithm （GA） and evolution strategy （ES）. Using permutation representation, the improved algorithm concentrates on constructing the initial population and on designing genetic operators, such as internal crossover（INCX） which swaps two list tasks within a schedule, improved crossover（IMCX） which exchanges tasks between two schedules, and migration which transfers a task from a list to another within a schedule as a kind of mutation operator. Finally, the simulation results of the algorithm and conclusions are given.