软硬件协同综合是嵌入式系统设计中的一个重要步骤。综合利用启发式算法和演化类算法的优点提出了一种混合量子遗传算法(HQGA)来解决软硬件协同综合问题，提高了求解质量和搜索效率，降低了计算代价。实验结果表明HQGA对软硬件协同综合问题的有效性：在得到相近结果的条件下，HQGA计算时间较量子遗传算法缩短50%以上；在计算相同代数的条件下，HQGA求解质量较量子遗传算法平均提高10%以上。

Hardware-software HW-SW co-synthesis is a key step of future design of embedded systems which consists of two NP-complete problems. So it is a really hard and challenging task to optimiza-tion algorithms. A new hybrid evolutionary algorithm,called hybrid quantum probabilistic coding genetic algorithm(HQGA),is proposed to implement the co-synthesis of large scale multiprocessor embedded systems. In HQGA,a heuristic algorithm is combined with the quantum probabilistic coding genetic algorithm(QGA)to enhance the performance on the hard task. The experimental results show that HQGA has better performance than both HA and QGA on large scale HW/SW co-synthesis problems.