AI 中文总结
提出用于自动模拟电路合成的SPECS遗传算法，借鉴NEAT原理，重新制定基因组表示并调整遗传算子，纳入布线约束和物种形成，在计算电路合成任务中，其解决方案质量和可靠性优于基准方法。
AI 中文摘要
我们提出了SPECS，一种用于自动模拟电路合成的遗传算法，可联合进行拓扑和尺寸优化。SPECS受增强拓扑神经进化（NEAT）启发，它最初用于合成神经网络。通过重新制定基因组表示并使遗传算子适应模拟电路领域，成功将NEAT核心原理应用于模拟电路合成。纳入特定电路布线约束以确保进化过程中设计有效且有物理意义，采用物种形成来保持创新并维持种群多样性。我们在由平方、立方、平方根和立方根函数组成的一组计算电路合成任务上评估该方法。实验结果表明，SPECS在所有任务的解决方案质量和可靠性方面均优于基准方法。合成电路及其原理图可在补充存储库中获取。
英文摘要
We propose SPECS, a genetic algorithm for automated analog circuit synthesis with joint topology and sizing optimization. SPECS is inspired by NeuroEvolution of Augmenting Topologies (NEAT), an evolutionary algorithm originally developed to synthesize neural networks. By reformulating the genome representation and adapting the genetic operators to the analog circuit domain, we successfully transfer the core principles of NEAT to analog circuit synthesis. Circuit-specific wiring constraints are incorporated to ensure valid and physically meaningful designs throughout the evolutionary process, and speciation is used to preserve innovation while maintaining population diversity. We evaluate the proposed method on a set of computational circuit synthesis tasks consisting of square, cube, square root, and cube root functions. Experimental results demonstrate that SPECS outperforms benchmark methods across all tasks in both solution quality and reliability. The synthesized circuits and their schematics are available in the supplementary repository.
