Optimization Algorithm of Evolutionary Design of Circuits Based on Genetic Algorithm

Xuejun Song, Yanli Cui, Aiting Li
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引用次数: 8

Abstract

For the convergence speed and scale bottlenecks of evolutionary design of circuits, the paper explores a new evolutionary method on the basis of genetic algorithm. Several optimization methods including fitness sharing, exponential weighting, double selection population, "Queen bee" mating, module crossover and optimal solution set are proposed to improve genetic algorithm. the new algorithm improved fitness evaluation method and genetic strategies. the experiment shows that the new evolutionary algorithm accelerates evolution convergence greatly, improves the adaptability effectively and expands the scale of evolved circuit obviously.
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基于遗传算法的电路进化设计优化算法
针对电路进化设计的收敛速度和规模瓶颈,提出了一种基于遗传算法的进化设计新方法。提出了适应度共享、指数加权、双选择种群、“蜂王”交配、模块交叉和最优解集等优化方法对遗传算法进行改进。新算法改进了适应度评估方法和遗传策略。实验表明,新进化算法大大加快了进化收敛速度,有效地提高了自适应性,并明显扩大了进化电路的规模。
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