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Optimization Algorithm of Evolutionary Design of Circuits Based on Genetic Algorithm

机译:基于遗传算法的电路进化设计优化算法

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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.
机译:为了解决电路进化设计的收敛速度和规模瓶颈,本文在遗传算法的基础上探索了一种新的进化方法。提出了几种适合度优化的方法,包括适应度共享,指数加权,双选择种群,“蜂王”交配,模块交叉和最优解集,以改进遗传算法。新算法改进了适应性评估方法和遗传策略。实验表明,新的进化算法极大地加快了进化的收敛速度,有效地提高了适应性,明显扩大了进化电路的规模。

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