摘要
针对基于遗传算法的数字电路的多目标优化设计问题,采用了带有均匀交叉的遗传算法,并且考虑到了多种制约条件,如:电路的复杂度,功耗,信号延迟等。结果电路的正确性,构成的复杂度,功耗和信号延迟等指标,可以采用适应度函数来评价。通过自动生出2位全加器的实验,验证了这种方法的有效性。实验结果证明,新提出的方法 ,在最优适应度函数值和平均适应度函数值方面,优于传统的遗传算法。
Aiming at the multi-objective optimization design of digital circuit based on genetic algorithm, a genetic algorithm with uniform crossover is adopted, and a variety of constraints are considered, such as the complexity of the circuit, power consumption, signal delay, etc.. Results the correctness of the circuit, the complexity, power consumption and signal delay and so on, can be used to evaluate the fitness function. The validity of this method is verified by the experiment of 2 full adder. Experimental results show that the new method is better than the traditional genetic algorithm in terms of optimal fitness function value and average fitness function value.
出处
《电子测试》
2015年第10期52-55,共4页
Electronic Test
基金
教育部留学回国人员科研启动基金资助项目(教外司留[2013]1792号)
河南省基础与前沿技术研究计划项目(132300410240
132102210140)
河南省教育厅科学技术研究重点项目(13B520903
12A510001)资助
关键词
进化算法
遗传算法
均匀交叉
进化硬件
电路设计
多目标优化
evolutionary algorithm
genetic algorithm
uniform crossover
evolutionary hardware
circuitdesign
multi objective optimization
