SPECS: Speciated Evolutionary Circuit Synthesis
2026-07-15 • Neural and Evolutionary Computing
Neural and Evolutionary Computing
AI summaryⓘ
The authors developed SPECS, a new method that uses genetic algorithms to automatically design analog circuits by optimizing both their layout and component sizes. They adapted ideas from a neural network design algorithm called NEAT to work for circuits, making sure the designs follow real-world wiring rules. Their approach keeps different design ideas alive during the process to encourage creativity. Tests showed that SPECS creates better and more reliable circuits for functions like square and cube roots compared to other methods.
genetic algorithmanalog circuit synthesisNEATtopology optimizationsizing optimizationspeciationevolutionary algorithmcircuit designwiring constraints
Authors
Yağız Gençer, Stefan Uhlich, Andrea Bonetti, Arun Venkitaraman, Chia-Yu Hsieh, Lorenzo Servadei
Abstract
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.