A NEAT Approach to Evolving Neural‐Network‐Based Optimization of Chiral Photonic Metasurfaces: Application of a NeuroEvolution‐of‐Augmenting‐Topologies Pipeline

Machine‐learning‐assisted optimization pipelines have recently emerged as efficient tools to accelerate the design of chiroptical properties for metasurfaces, yet their performance strongly depends on the choice of neural‐network (NN) architecture. In this work, we integrate the NeuroEvolution‐of‐Augmenting‐Topologies (NEAT) algorithm into an established deep‐learning optimization framework for dielectric chiral metasurfaces. NEAT autonomously evolves both network topology and connection weights, enabling task‐specific architectures without manual tuning, whereas the reinforcement‐learning strategy in our framework evolves knowledge of the solution space and fine‐tunes a model's weights in parallel. Using a pipeline‐produced dataset of 9600 simulated GaP metasurface geometries, we evaluate NEAT under varying input dimensionalities, feature‐scaling methods, and data sizes. With standardized feature scaling yielding the most consistent performance for both examined output dimensionalities, the relatively compact NEAT‐evolved NN models, when integrated into the full optimization pipeline, achieve similar or improved predictive accuracy and generalization compared to the initially employed dense few‐layer perceptrons. Accordingly, these resource‐efficient models successfully perform inference of metasurfaces exhibiting strong circular dichroism in the visible spectrum, allowing for transfer learning between simulated and experimental data. This approach demonstrates a scalable path toward adaptive, self‐configuring machine‐learning frameworks for automated photonic design, both as a standalone and as a building block for agentic artificial intelligence (AI).