MorphoNAS: Embryogenic Neural Architecture Search Through Morphogen-Guided Development

A novel MorphoNAS approach is proposed for deterministic neural network growth through morphogen-guided self-organization based on the Free Energy Principle, reaction-diffusion systems, and gene regulatory networks. The developmental model is investigated, where compact genomes encode only morphogen dynamics and threshold-based cellular rules enabling single progenitor cell transformation into complex neural architectures. Full success (100%) is achieved in evolutionary search for genomes generating predefined graph configurations with 8-31 nodes. Minimal functional controllers (6–7 neurons) for the CartPole task are obtained under network size minimization pressure with 94% population success rate. The results demonstrate that biologically plausible developmental rules can serve as an effective mechanism for automated neural architecture search. Keywords: MorphoNAS, morphogenesis, neural architecture search, reaction-diffusion systems, gene regulatory networks, evolutionary algorithms.