中文摘要：

针对电路进化设计时传统进化算法收敛速度慢且易陷入局部最优解等问题,模拟人体免疫系统的机制,设计了一种改进的免疫遗传算法用于逻辑电路的进化设计.首先建立电路进化设计模型,对种群中抗体进行多目标适应度评估;然后改进抗体的选择机制并将精英抗体作为记忆单元保存;最后引入自适应交叉、变异策略保持抗体多样性并提高算法的收敛性.实验结果表明：该算法具有较强的全局搜索能力,能有效地减少搜索到全局最优解的迭代次数,并设计出新颖、高效的电路结构.

英文摘要：

In order to overcome the problems with the traditional algorithms in designing logic circuits such as slow evolution speed and premature convergence, by simulating the body＇s immune system mechanisms, an improved immune genetic algorithm is proposed for the evolutionary design of logic circuit. Firstly, it takes multi-object fitness evolution to assess the individuals of population. Then, the individuals with high fitness are stored by elitist strategy as a memory unit for improving the selection mechanism of the individual. Finally, by adopting the adaptive crossover and mutation strategy, the diversity of individuals is maintained and the convergence of the algorithm is improved. The search ability of the proposed method is tested using the two-bit multiplier. Compared with the genetic algorithms and immune algorithms, the experiment results show that proposed design has strong global search capability, effectively reducing the convergence algebra of optimal solution.