CAJ | 학술논문

针对软硬件划分问题，研究了一种优化的遗传算法，提出硬件倾向度的概念，用于遗传算法初始群体的生成，减少了初始解的随机性和搜索的盲目性；在遗传算法过程中，使交叉变异概率随着遗传过程由大变小，保证早期具有较大的搜索空间，后期又能保留较好的解，使用动态结束条件自适应结束遗传算法。与对比算法相比，该算法的效率较高，且在大规模问题求解上能够获得更优解。
침대연경건화분문제，연구료일충우화적유전산법，제출경건경향도적개념，용우유전산법초시군체적생성，감소료초시해적수궤성화수색적맹목성；재유전산법과정중，사교차변이개솔수착유전과정유대변소，보증조기구유교대적수색공간，후기우능보류교호적해，사용동태결속조건자괄응결속유전산법。여대비산법상비，해산법적효솔교고，차재대규모문제구해상능구획득경우해。
For hardware and software partitioning problem, the paper studies a kind of genetic algorithm optimization and the concept of hardware tendency degree of genetic algorithm used to generate the initial population, which can reduce the ran-domness and blindness searching initial solution. During genetic algorithm process, the probability of crossover and mutation genetic processes changes from large to small so as to ensure a larger early search space, and also allows the latter to retain a better solution, ends the use of dynamic adaptive genetic algorithm end condition. Compared with the comparison algorithm, the higher efficiency of the algorithm is shown and it can get better solutions on a large scale problem.